Volume 41 (2022), Number 3STAR – State of The Art Report

EUROVIS 2022
S. Bruckner, C. Turkay, and K. Vrotsou(Guest Editors)

A Survey of Visualization and Analysis in
High-Resolution Connectomics
Johanna Beyer∗1 , Jakob Troidl∗1 , Saeed Boorboor2 , Markus Hadwiger3 , Arie Kaufman2 , and Hanspeter Pfister1
1 Visual Computing Group, Harvard University
2 Department of Computer Science, Stony Brook University
3 Visual Computing Center, King Abdullah University of Science and Technology(∗ ) indicates equal contribution

Abstract
The field of connectomics aims to reconstruct the wiring diagram of neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the neuronal connectivity, however, relies on many individual steps, startingfrom high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In
this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, fromdata acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently
integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools andpublicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
CCS Concepts• Human-centered computing → Visualization systems and tools; Scientific visualization; • Applied computing → Life andmedical sciences; Systems biology;

1. Introduction
In recent years, neuroscience has made significant advances inunderstanding the brain’s structure and function based on novel
high-resolution data acquisition techniques. Scientists aim to tackle
questions of brain physiology, how diseases of the brain develop,
and how the mind forms consciousness. The field of connectomics,
in particular, approaches these questions by looking at the detailed
connectivity between neurons at high-resolution to extract thebrain’s wiring diagram. The ultimate goal is to map and decipher ahuman’s entire neuronal wiring, called the connectome [Seu12a].
Advances in tissue imaging such as high-resolution volume electron microscopy (EM) [BB12] enable this highly-detailed analysis
by allowing scientists to collect unprecedented amounts of neuronaltissue data at nanometer resolution. This high-resolution data allows scientists to reconstruct individual neurons and all their connections (synapses) to other neurons. In addition, structural biologists and developmental neuroscientists are also interested in spatial and neighborhood analysis of neurons and analyzing the structural plasticity of how synapses and neurons change over time.
One of the main challenges in connectomics is the amount ofdata that scientists need to collect, process, and analyze. Even asingle cortical cubic millimeter of mouse brain tissue consists ofaround 75, 000 neurons and 523 million synapses [CBB∗ 21], mak© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John
Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

ing brain connectivity exceptionally difficult to understand and analyze. Only the advent of automated high-throughput imaging techniques and automated computational segmentation tools have madeanalyzing the neuronal connectivity within brain volumes of a cubic
millimeter or more now possible [YBB∗ 20, SCJB∗ 21, CBB∗ 21].
However, for neuroscientists to analyze these large and complex
3D datasets, they need scalable visualization methods throughout
the entire connectomics data processing pipeline (see Fig. 1) thatallow them to interact with their data intuitively and flexibly. For
example, visualization can be used for interactive image quality
assessment during the image acquisition stage [HHM∗ 17] or forinteractive proofreading of the segmentation data. Visualization is
also vital for data exploration, such as interactive spatial and connectivity analysis tools.
This survey discusses how visualization and visual computing
can be integrated into the connectomics pipeline to help scientists
in analyzing their high-resolution and large-scale data volumes.
1.1. Survey Scope and Previous Surveys
This state-of-the-art report summarizes previous research and recent developments in visualizing large, high-resolution biological
data volumes. We describe and categorize visualization methods
for each step of the connectomics pipeline, including novel ap-

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Figure 1: The connectomics pipeline: In this survey, we discuss visualization approaches for each step of the connectomics data processing
pipeline. Visualization is not just important for the final analysis step but vital for efficient image acquisition, registration, segmentation, andproofreading of segmentations.
proaches to visual proofreading, large-scale volume exploration,and connectivity-focused visualization methods.

sis. The remainder of our survey follows the different steps of theconnectomics pipeline.

In addition to the specific visualization methods, we refer to themost relevant work in image acquisition, registration, and segmentation. Furthermore, we discuss relevant topics in large-scale volume visualization and connectivity-focused visualization. We further outline methods for quantitative analysis of spatial and connectivity data and refer to relevant STARs in those areas.

Section 3 talks about data acquisition for microscale andnanoscale datasets, as well as how visualization can support thedata acquisition process. Next, we discuss image alignment andregistration methods (Section 4) before outlining current methods
for semi-automatic and automatic image segmentation (Section 5).
To further improve segmentation results from automatic methods,
many different semi-automatic proofreading methods have been
proposed, which we discuss in Section 6.

We limit our discussion primarily to visualization-centered techniques for high-resolution 3D light- and electron-microscopy datathat can depict individual neurons as well as individual synapses,
respectively. In contrast to a previous survey on visualization inconnectomics from 2012 [PKB∗ 12], the present work focuses on
micro- and nanoscale datasets and the visualization of individual
neurons and synapses. Developments in high-throughput data acquisition and automatic segmentation have paved the way for novel
approaches to semi-automatic proofreading and connectivity-based
visual analysis at the level of individual synapses. These novel andscalable visualization methods for micro- and nanoscale neuronal
structures differ significantly from previous macro-scale connectivity visualization approaches, such as visualizing fiber tracts andbrain regions. The latter we do not discuss in this survey.
The most relevant previous surveys in the area of visualization include a micro-, meso-, and macro-scale connectomics report [PKB∗ 12], a survey on large-scale GPU-based volume visualization [BHP15], surveys on modern (scientific) visualization on the web [MKRE16, FH20], and a survey on macroscopic brain circuit visualization [CSC18]. Other relevant surveys include biomedical image registration [VMK∗ 16], segmentation [MBK∗ 19, LRL∗ 21], motif discovery in graphs [YFZ∗ 20] andsubgraph counting [RPS∗ 19].

1.2. Survey StructureThis survey gives an overview of the current state of the art in thevisualization and analysis of high-resolution neuronal data for connectomics. We start with an introduction to fundamentals in neuroscience in Section 2, outlining the biological background and theconnectomics data processing pipeline, from acquisition to analy-

While visualization approaches play a crucial role in all steps ofthe connectomics pipeline, the exploratory analysis of registered,segmented, and proofread volumes relies on many diverse visualtechniques. To this end, we discuss visual exploration and analysis methods in Section 7. After outlining scalable data structures in
Section 7.1, we discuss methods for spatial exploration, including
approaches for large or segmented volumes, in Section 7.2. Next,
we outline visual methods for exploring neuronal connectivity inthese data volumes, going from node-link diagrams to more specialized neuron-centric views in Section 7.3. In Section 7.4, we discuss methods for a more quantitative analysis of neurons and spatial
neighborhoods, including visual query systems that interactively
drill into the data. Finally, we outline visualization methods used
for the communication of connectomics insights in Section 7.5.
To complete this survey, we provide a list of popular and publicly available datasets, as well as open-source tools to help foster future research in connectomics and visualization in Section 8.
We also list datasets, tools, and surveyed papers on our website (connectomics-vis-survey.github.io). Finally,
we look at future trends and open problems in Connectomics visualization in Section 9 and conclude in Section 10.
2. Neuroscience Fundamentals
We first introduce the fundamentals in neuroscience to clarify themost important biological concepts for connectomics. Then, we
outline the connectomics pipeline, which is the typical data acquisition and processing workflow in connectomics research.
Modern neuroscience began around the end of the 19th century© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Neuron:Dendrites

Cell
body

Axon

Mitochondrion
Dendrite
SynapseSpine
BoutonAxon

Figure 2: Cajal’s original drawing of neurons as individual cells.
with the discovery of neurons. Santiago Ramón y Cajal was the firstto report the delicate anatomy of the nervous system (see Fig. 2)
and argued that neurons are information processing units that form
connections and propagate electrical impulses to accomplish theirvarious functions. Thus, laying the foundations of modern neuroscience and the conceptual framework for connectomics [SL16].
The term connectome originally meant “a comprehensive structural description of the network of elements and connections forming the human brain” [STK05], but has since been extended inits definition to include any systematic account of connections,from local circuits to networks constituting entire nervous systems [SL16]. The corresponding research field connectomics uses
computer-assisted and high-throughput techniques for image acquisition and analysis for the structural mapping of neural circuits.
Current research is also studying how the brain’s functional architecture rises from the underlying structural circuit architecture.
2.1. Biological Background
The adult human brain consists of approximately 100 billion neurons with each neuron having up to thousands of neural connections to other neurons. A recent dataset of one cubic millimeter of ahuman cerebral cortex contained 50, 000 neurons with 130 million
interconnections [SCJB∗ 21]. Neurons process and transmit information as electrical impulses by forming synaptic connections withother neurons. The size and form of neurons can vary widely, but
typically, neurons receive input on tree-like structures called dendrites and transmit signals via their axon, a long tubular structurethat transmits signals away from the cell body (soma) towards otherneurons (see Fig. 3). Axons and dendrites are collectively calledneurites and make up the topological skeleton.
A synapse is the small gap between two neurons, where nerve
impulses are relayed. A typical mammalian synapse consists of apresynaptic terminal (a bouton) on the side of the axon that releases
neurotransmitters when activated, a post-synaptic terminal at theside of the dendrite, and the synaptic cleft between dendrite andaxon. The signaling process is partly electrical and partly chemical, where a nerve impulse (i.e., action potential) travels along theaxon and activates synaptic connections. A synapse can either be
excitatory or inhibitory, depending on whether a spike on the axon
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 3: Structure of neurons and their connecting synapses. Neurons consist of a cell body, dendrites, and an axon. Most neurons
receive signals via dendrites (orange) and send out signals via theaxon (blue). Synapses transmit the signal between two neurons.
Neurons contain different cell organelles, such as mitochondria
that provide energy for cell metabolism.
increases or decreases the chance of provoking a spike in the receiving dendrite. Compared to the size of a neuron in the human body(i.e., a fraction of a centimeter to a meter), a synapse is extremely
small (20-40 nm) [LD11]. Therefore, to capture synapses, tissue
needs to be imaged at a very high resolution, resulting in large
datasets. In addition to synapses, subcellular structures of neurons,so-called cell organelles, are also of high interest to neuroscientists.
Mitochondria, for example, are responsible for providing energy to
the cell and its synapses. Interestingly, only half of the cells in the
mammalian brain are neurons. The rest are glia cells, which provide supporting features to neurons.
Neurons, their cell organelles, and synapses exhibit high structural variability. For example, synapses can vary by the positionof the post-synaptic terminal (i.e., either on a dendrite’s shaft ora small extension called a dendritic spine) or by the number ofvesicles (cell organelles containing neurotransmitters) in the bouton. However, it is an active area of research on what influences orcauses the variability of these attributes and how they impact thefunction of a neuronal circuit. Therefore, in addition to connectivity analysis, scientists are interested in trends and correlations andlooking at individual neuronal structures, synapses, and attributes.
2.2. Connectomics Pipeline
High-resolution connectomics relies on many different processing
steps to go from the original tissue samples of neuronal data to thereconstructed and analyzed wiring diagram. This process is generally called the Connectome pipeline [LPS14, Seu12a] and is depicted in Fig. 1. Throughout this process, at each step, interactive
and scalable visualization is key to helping scientists handle theirlarge and complex datasets. This survey follows the individual stepsof the connectomics pipeline and focuses on visualization methods
and challenges in each step.
Data Acquisition. Data acquisition starts with a 3D tissue sample that is chemically preserved to lock proteins and molecules that

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form neuronal structures. Next, the tissue sample is cut into verythin slices using a microtome and, subsequently, each slice is imaged with a microscope. Light microscopes can acquire images ofa xy resolution of 200 nm. EM is necessary for higher resolutionsof up to 4 nm in the xy plane and around 30 nm in the z-dimension.
EM also densely captures details such as synapses.
Alignment & Registration. To image a large tissue slice, microscopes typically acquire image tiles of a fixed size (e.g., 12,000 ×
12,000 pixels). In a post-processing step, these individual 2D image
tiles need to be aligned and stitched to combine them into a single
large 2D section. Registration, on the other hand, takes a stack of
2D sections as input and aligns them to reconstruct a 3D volume
representing the original 3D tissue block.
Segmentation & Proofreading. Reconstructing the wiring diagram of all neurons in a tissue block requires labeling (i.e., segmentation) of cell membranes and synapses. The gold standard forsegmentation is manual labeling, requiring expert users to mark individual pixels as belonging to certain structures. This approach,however, quickly becomes infeasible for large volumes with millions of neurons. Therefore, more and more semi-automatic andfully automatic segmentation methods have been developed recently. While automatically generated segmentations are becoming
increasingly accurate, they still require proofreading by humans tocorrect mistakes and verify the reconstructed connectivity graph.
Semi-automatic proofreading methods aim to streamline this process by pointing users to likely segmentation errors in the dataset.
Exploration & Analysis. The final steps in the connectomics
pipeline consist of visual data exploration and analysis. Depending on the type of data available (e.g., light microscopy or EM,
segmented or unsegmented), different visualization methods exist
to explore the data in more detail. Visualization approaches range
from the visual exploration of the raw imaging volumes to the analysis of abstract connectivity graphs or query-based visual analysis
approaches that combine spatial data, connectivity data, and anylabeled or extracted metadata.
3. Data Acquisition
Advances in high-resolution imaging technologies enable the acquisition of brain connectivity information across multiple temporal and spatial resolutions. Based on the scope of this survey, we
provide an account of the micro- and nano- scale modalities used
for brain data acquisition. For macroscale modalities that captureneural fibers and their connectivity information at centimeter andmillimeter resolutions, we refer the reader to detailed surveys by
Pfister et al. [PKB∗ 12], and Chen et al. [CSC18].
3.1. Sample Preparation
Steps for sample preparation differ based on the imaging modality. Brain tissue samples are tagged using various labeling techniques for optical imaging to capture the structural information ofindividual neuronal cell types or their projective trajectory. Specifically, neurons are labeled using single or multi-colored stains or
by breeding transgenic subjects to produce photophysical fluorescent proteins that express different colors on the exposure of

Figure 4: Left to right: Wide field (WF) microscopy slice, EM slice,
and registered EM volume showing 2D image sections.
light [LLS08]. Additionally, to capture relationships between organization and function of neural circuits, specimens are injectedwith viruses, such as Rabies [CL15], that spread selectively between synaptically connected neurons.
For imaging nanoscale structures using EM, tissue samples areembedded in plastic to prevent its destruction during slicing. Next,
the sample is stained with heavy metals that interact with electronsin ways that electron microscopes can detect. After staining, thetissue sample is either first physically cut into thin slices using a
microtome and then imaged [HMS∗ 14], or the microscope images
and destroys thin sections of the sample progressively [TKH∗ 12].
3.2. Microscale Brain Imaging
Light microscopy has been used to map and study neuronal circuits
at a micrometer scale for a long time. Neuroscientist Cajal was able
to identify connectivity patterns of Golgi stained neuronal circuits
for the first time [Val70] using microscopic resolution sufficient toimage single neurons.
Wide Field Microscopy. The choice of the microscope is greatly
dependent on sample thickness and signal-to-noise tolerance.
Wide-field (WF) microscopy exposes a whole tissue sample to alight source before capturing the image [BJA∗ 19]. With its large
field of view, WF can image a sample in a short time, albeit for
thin, sectioned slices (< 30µm). It collects light emitted from the
focal plane and illuminated layers above and below the focal plane.
As a result, the acquired images suffer from a degraded contrast between foreground and background due to out-of-focus light swamping and low signal-to-noise ratio (see Fig. 4, left). Image processing
techniques aim to resolve the blur and focus (see Section 3.4).
Confocal Laser Scanning Microscopy. In contrast to WF, a confocal laser scanning microscope utilizes laser units as the light source
to excite the fluorescent stains of a sectioned sample (30-70 µm)
and acquires the resulting image following a pinhole design principle [Pad00]. As such, the confocal system can block out any outof-focus light. Although this produces sharp images, some information is lost due to the pinhole blocking out-of-focus photons that
may have originated from the focal plane.
Both confocal and fluorescence WF microscopy systems produce raw 3D data by sequentially changing the focal plane along
the z direction (that is, along with the depth of the sample) and stack
the generated 2D images atop each other. Typically, modern superresolution microscopy systems can achieve a lateral xy resolutionof around 200 nm and an axial resolution of around 600 nm [Wil].
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

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Advanced Methods. Sectioning a brain specimen into thinner samples increases the probability of the structures being severed at multiple points across consecutive slices. Advanced microscopy technologies, such as light-sheet and two-photon tomography, allow
imaging of thicker or whole-brain samples. Light-sheet microscopy
is a modification to wide-field fluorescence technology such that it
has intrinsic optical sectioning capabilities by automatically moving the sample through a single plane sheet of light [DLS∗ 07].
Serial two-photon tomography [RKV∗ 12] is an ex-vivo imaging technique that combines serial imaging and sectioning. The
simultaneous absorption of molecules by two photons allows optical sectioning without absorption above and below the plane of
focus. This eliminates the need for a pinhole. Though this method
offers increased depth penetration, the custom addition of a builtin vibrating blade microtome creates undisturbed images within thesame space, as the sectioning occurs after the imaging. Finally, withthe advancement of “super-resolution” microscopy, optical examinations of nanometer-scale phenomena using light microscopy is
now possible (e.g., [HW94, SEG15]).
While the mentioned modalities have resolutions that are notsufficient to resolve synapses, expansion microscopy (ExM) physically expands brain tissues to overcome the resolution limitation of light microscopy [CTB15, WZB19]. Additionally, imaging neurons at a micrometer scale facilitates capturing functional
networks of neuronal systems [WTY∗ 15, RHC20], trace synaptic
circuits [LWK∗ 07], and analyze its geometry and functional connectivity across disease and aging [BMA∗ 21]. Moreover, light microscopy has also been used recently for the connectivity analysis
of defined cell types by using spectral connectomics [SHW∗ 20].

3.3. Nanoscale Brain Imaging
EM is the only modality that can densely image biological tissues
at a nanometer scale (see Fig. 4). Short wavelengths of high-energy
electrons provide sufficient resolution for EM to capture the finest
details of synaptic connections and organelle structure.
The main challenge to imaging large brain volumes using EM include throughput time, alignment, and contrast. Early technologies
require a sample to be sectioned into ultrathin slices, which are thenaligned on a tape and imaged at a pixel resolution of 4×4 nanometers [LPS14]. Transmission electron microscopy (TEM) [BLK∗ 11]
utilizes camera arrays that allow a fine lateral resolution, aiding in
the identification of fine structural details. Newer techniques, suchas serial-blockface scanning EM (SEM) [DH04] and focused ionbeams (FIB-SEM) [KMWL08] are block-face methods that successively image and section each layer of the sample block. This allows better alignment, thus saving computation time. Specifically,
FIB-SEMs are designed to section the sample automatically. Multibeam scanning electron microscopes (mSEM) [EMS∗ 15, HPJ∗ 20]
image larger fields of view, enabling the acquisition of big tissue
samples in a reasonable amount of time. For instance, ShapsonCoe et al. [SCJB∗ 21] used an mSEM with 61 ion beams to image
10,000 µm2 simultaneously as a hexagonal shape. Overlapping image tiles are then stitched together for larger fields of view.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 5: Visualization of multi-beam SEM acquisition and alignment with MBeam viewer [HHM∗ 17]. a) 61 image tiles per multibeam field of view (MFOV). Each individual image is 3, 128 ×
2, 724 pixels in size, resulting in roughly 30k × 26k pixels for each
MFOV. b) 55 MFOVs are stitched together into one 200k × 170k
pixel section. c) zoomed-in view to evaluate coarse alignment.

3.4. Visualization for Data Acquisition
Image quality assurance is essential in preparing a digital tissue
volume. MBeam viewer [HHM∗ 17] is a web-based visual tool toassess imaging quality and contrast during data acquisition of EM
images. EM collects multiple tiles per imaging section that need
to be stitched together, and stitching greatly depends on the image
quality and a uniform contrast across the tiles. Typical quality problems are the inconsistent vertical position of the sample (primarilyin ATUM-SEM), unfocused images due to slice height variations,and external particles on the imaging specimen. MBeam viewer allows immediate and remote inspection of the image tiles by juxtaposing them on a 2D canvas. On-the-fly mipmapping enables interactive zoom and pan and the rapid assessment of the image tiles’
quality (see Fig. 5). Modern EM [EZ18] software, such as for the
Zeiss SEM, integrates visualization for browsing and filtering theimage collection and can even create 3D scans of a sample.
Since EM imaging produces roughly a petabyte of image data
per cubic millimeter, data storage poses a significant cost factor anda limitation for volume visualization algorithms. Therefore, it is desirable to compress imaging volumes as much as possible without
losing details needed in the rest of the connectomics pipeline. Minnen et al. [MJB∗ 21] present a machine learning-based denoising
approach for EM images. Compressing the denoised images leads
to a 17-fold data size reduction with no loss in segmentation andnegligible loss in synapse prediction accuracy. They use a UNET
architecture with residual connections to learn a denoising model.

4. Alignment and Registration
Raw microscopy data needs to be processed to recover the 3D volume of the specimen. Challenges during data acquisition, such as
slicing and deformation, and a limited field-of-view only allow theimaging of small image tiles. Mosaicing describes the alignmentand stitching of individual imaged tiles acquired in the lateral directions (i.e., xy-plane). This is followed by the axial registration(i.e., z-direction) of sections through the sample volume. Imageregistration is well studied, using either intensity- or feature-based
techniques. In this section, we focus on methods specialized forprocessing brain volumes imaged with optical and EM modalities.

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4.1. 2D Alignment and Stitching
High-resolution microscopes have lenses with a small field of view.
Thus, large biological tissues are imaged by laterally acquiring
smaller image tiles in the xy plane, with partial overlap. Alignment techniques aim to resolve these overlapping tiles and stitch
them to reconstruct a complete 2D image section. In the literature,
this step is also often referred to as mosaicing or montaging. Algorithms for alignment and stitching address two important challenges. First, high-resolution microscopy generates a large amountof data. A single 2D image slice imaged using light microscopy isin the order of tens of gigabytes, while a slice imaged using EM ishundreds of gigabytes. Therefore, alignment algorithms need to behighly optimized or parallelized to recover the transformation parameters for all image tiles in a reasonable amount of time. Second,
the sample preparation and imaging workflow are fallible. Often
there are tears, folds, or other deformities in the mounted sample.
As a result, these algorithms need to consider missing information,
artifacts, and inconsistent lighting and contrast conditions.
Broadly, the following general steps are adopted to align andstitch a grid of image tiles: (1) identify tile pairs and their overlapping region, (2) estimate candidate displacement values between
adjacent tiles, (3) apply fine adjustment optimizations to reduce errors in the stitched image, (4) stitch tiles to produce the mosaic, andin some works, (5) post-process the component images together toyield a seamless final result. The literature identifies two main approaches for aligning and stitching image tiles.
Feature-based approaches [CHP∗ 06,SCHT10] identify matching features in adjacent tiles to compute image translations. Thisapproach can be sensitive to feature sparsity in the overlapping
regions of adjacent tiles. Thus, some methods make assumptions
about the data, e.g., for aligning blob-like structures [BAS∗ 96],
fine-filaments [AKCL∗ 03], or contours [BLLF06].
(Pixel) correlation techniques [Gre09,ERP∗ 09,TKG∗ 10,BI12,
CMB∗ 17] can be more general as they utilize domains such as image frequency by applying Fourier transforms, or pixel intensity
by applying cross-correlation or mutual information. Similarly, thisapproach assumes that images have enough pixels with unique frequency or intensity components in the overlapping areas. As such,the choice of method depends on the overall image content and thecharacteristics of matching features.
Hybrid approaches [ERP∗ 09, TLB∗ 11] make use of bothfeature-based and pixel correlation methods for a coarse-to-finealignment. Using this approach, Tsai et al. [TLB∗ 11] have improved the accuracy, robustness, and scalability in aligning lightmicroscopy image tiles. They determine potential tile pairs by extracting features such as corners and edges from 2D maximumintensity projection (MIP) images [Low04] and find pair matches
using the Dual-Bootstrap Iterative-Closest Point algorithm (DBICP) [YSST07]. An affine transformation between tile pairs is computed to account for spatial distortion between pairs. Finally, tostitch the tile pairs into a single volume, they employ pixel-based
normalized cross-correlation (NCC) minimization and a global
consistency approach [CSRT02].
Preparing brain samples often cause tears or folds, creating gaps
in the image continuity. As a result, an overlap between image pairs

can not always be guaranteed. To address this challenge, Yigitsoy
and Navab [YN13] have introduced an approach based on structural continuity beyond image boundaries. A tensor voting on theorientation and saliency properties of the extracted structures isused to infer artificial overlaps in the extended regions. The inferredaligned structures are then used to estimate multi-scale transformations for the optimal alignment between the tiles.
Tools for 2D Alignment and Stitching are designed to minimize the overall computational workload of large image volumes
while also keeping track of tile-pair alignment quality and allowing
manual intervention.
XuvTools [ERP∗ 09] is an automatic 3D stitching software that
adopts a coarse-to-fine strategy, combining both feature and pixel
correlation techniques. Assuming no information about the tile sequence, XuvTools uses a multi-scale phase-only correlation to estimate a coarse position of the tiles. Using a GUI, users manually position the tiles in a grid layout. For fine-scale alignment, it detects
salient points that would appear in the overlap region and maximizes NCC coefficients of multiple smaller patches around thesalient points. Once tiles pairs are determined, absolute tile positions are computed by globally minimizing the displacement values. Finally, artifacts due to changing intensities at the border of
the tiles are alleviated by applying bleaching correction to each tilepair. While this process is fully automated, XuvTools displays the
progress of alignment and stitching at every iteration. If a stitchingerror is identified, users can adjust parameters for the correlationthreshold and the size and search radius of correlation windows torecompute the fine alignment step.
TeraStitcher [BI12] is designed to stitch tera-sized tiled microscopy images on consumer hardware. To efficiently estimate
the relative displacement of tile pairs, adjacent tile pairs are divided into user-defined substacks, and 2D maximum intensity projections of the tiles along the three dimensions are used to compute the NCC coefficients. For the optimal global placement of tile
pairs, a minimum spanning tree approach by Yu and Peng [YP11]
is employed. Finally, overlapping regions are blended using twophase-shifted sinusoidal functions for a complete mosaic. To further improve processing and stitching time, Bria et al. [BBGI19]
use a CUDA multi-process parallelization strategy. TeraStitcher allows manual intervention to refine stitching metadata by providing(1) a preview feature to stitch user-selected portions of data and(2) graphical metadata models for a more comprehensive analysis.
Users can detect and locate abnormalities by inspecting the stackborders using the preview.
Although some microscopes typically provide approximate tile
locations, in practical settings translation stages can still exhibit
inaccuracies. To this end, Hörl et al. [HRRP∗ 19] have developed BigStitcher, designed specifically for gigapixel light-sheet
microscopy datasets that solves for tile pairs in non-regular grids
containing empty images and multiple independent samples. Using
downsampled image tiles, all possible shifts for overlapping pairs
are first calculated using phase correlation, called links. In the optimization step, an additional attribute of strong (confirmed) andweak (estimated) links is introduced to solve for the global optimal tile placement. By optimizing both link types, strongly linked
regions are accurately aligned, while an optimal alignment is de© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

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termined for weakly linked groups of tiles. The entire process isinteractively displayed, enabling users to verify, interact and potentially guide the proper alignment of complicated datasets.
In studying the alignment workflow of pairwise displacement
followed by global placement, Chalfoun et al. [CMB∗ 17] foundthat residual errors can be introduced when the raw data has sparsefeatures or information for feature-based and intensity correlation
methods, respectively. To address this, they have introduced Microscopy Image Stitching Tool (MIST), a grid stitching techniquethat minimizes stitching errors by estimating the microscope mechanical stage model parameters and using them to improve thesearch space of the global tile placement optimization step. MIST
uses a multicore hybrid CPU/GPU implementation to handle terabytes of microscopy data.
Simply warping one tile onto another using the displacementtransformation can introduce artificial deformations in the final
stitched output. To this end, Saalfeld et al. [SFCT12] have introduced a technique for EM images that aligns corresponding key
points and formulates an elasticity constraint on local neighborhoods to solve for a nonrigid deformation. The elasticity constraint
is implemented by representing image tiles as a triangular mesh ofa spring-connected particle system. Each vertex of the spring meshsearches for corresponding key-point locations in the adjacent tile
using pairwise block-matching, and the springs tend to maintain arigid transformation across the overlapping tiles, penalizing distortions. Such a constraint allows the alignment of arbitrarily large
image tiles without propagating transformation errors. Haehn etal. [HHM∗ 17] have developed a visualization tool for this process,called RHAligner, which provides an abstracted visualization of
stitched sections and allows for quick assessment of the alignment
process. RHAligner expands the algorithm to solve for a hexagonal
grid and optimizes it by adopting a parallel implementation.
4.2. 3D Registration
Brain specimens have to be physically sectioned into a series ofslices for most imaging modalities. Thus, to extract 3D structuraland functional information, the serial sections need to be registeredalong the z-direction (or imaging direction). However, the processof physical sectioning severs structures at the slicing interface, destroying continuity between consecutive sections and resulting intissue deformation. In contrast, modalities that adopt a block faceapproach do not suffer from this information loss [HXL∗ 15].
Some rudimentary approaches to simplify registration include introducing intrinsic or imposed fiducial markers in thesample [HML∗ 95, BMV05, PYD04], defining anatomical landmarks [SPB∗ 15], or attaching additional imaging modalities [J∗ 09]
to be used as a priori information.
Beyond an a priori approach, 3D registration techniques are designed to recover a volume that results in the natural progression offeatures through successive sections [JWC∗ 06]. Within the scopeof optical microscopy, Lee and Bajcsy [LB08] have proposed registering sections by tracking the trajectory of salient cylindrical structures in each section and across adjacent slices.
Tools for 3D Registration. Peng et al. [PCL∗ 11] have developed BrainAligner for registering light microscopy image slices© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

that first perform robust global affine registration by transforming
them into a common coordinate system using a predefined brain
atlas [BIP15], followed by nonlinear local alignments using predefined landmarks. For local alignment, predefined landmarks areused to generate a thin-plate-spline warping field [Boo89]. Furthermore, BrainAligner allows adding custom landmarks to help optimize or improve critical alignments.
Some studies [HPJ∗ 11, DWG∗ 09, LFP11] have suggested using
traced neurons for registration. Essentially, the process of neuron
tracing (see Sec. 5.2) generates a concise and less noisy representation of neuron morphology compared to using the raw microscopy
volume for registration. The Filament Editor [DHO14] and NeuronStitcher [CIdC∗ 17] are two widely used interactive registration
and alignment tools that assemble adjacent microscopy sections using traced neurons and allow fine-tuning by providing a visualization of complicated neuron fragments.
NeuroConstruct [GBM∗ 21] adopts a hybrid approach of using intensity correlation (for coarse registration) and feature-basedalignment (for fine tuning) to register segmented light microscopy
brain volumes. Following global registration, similar to Yigitsoy
and Navab [YN13], a tensor-based method is used to propagate
and register exiting/entering neurites across adjacent sections. Finally, the reconstructed volume can be interactively fine-tuned byclicking on corresponding entering/exiting neurites.
In the EM domain, Fialia [Fia05] has developed Reconstruct, toperform both manual and automatic registration on serial section
EM slices. For manual registration, users can progressively define
and adjust transformation parameters (translation and rotation). Results are visualized by blending or flickering adjacent sections. For
automatic registration, Reconstruct treats neuron traces in sections
as fiducial markers and computes the transformation that minimizes
the distance between the centroids of the traces in section pairs.
Users can view the registration result and make manual edits to fix
misalignments or distortions. Adjustments made to one section pairare then propagated to the rest of the series.
TrakEM2, developed by Cardona et al. [CSS∗ 12], is a widelyused open-source software for neural circuit reconstruction fromterascale EM serial sections. For its automated registration pipeline,
sections are registered by first extracting SIFT features [Low99]
to estimate a linear transformation, followed by elastic alignment [SFCT12] to compensate for non-linear distortion. Alternatively, for manual registration, feature correspondences are assigned using a GUI interface. Local alignment and transformation
errors are interactively fixed using point-click-and-drag manipulation aided by transparent overlays of section pairs. Brain volumes
are viewed either as low-resolution images of large fields of viewor as high-resolution images for user-defined areas of interest.
In addition to mosaicing, the elastic registration technique [SFCT12] discussed in Sec. 4.1 can also be used for registering adjacent section series. The block matching algorithm explores transformation parameters in all sections of a local neighborhood of each vertex of the defined spring mesh. Using this, Haehn
et al. [HHM∗ 17] have developed a plugin named RHAligner for
their Butterfly middleware that registers EM sections and provides
a visualization to monitor, debug, assess, and fine-tune the results.
Since identifying features incorrectly can result in the failure of all

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termination. Current techniques aim to identify intensity distribution features and determine the tracing direction for branching neuronal morphologies. Methods to address these goals include optimal seed-points path finding [PLM11, LBA11, QZL∗ 16], model
fitting [WNTR11, LQX∗ 19], fast marching [BR14, YHL∗ 19],
distance-tree hierarchical pruning [XP13], and deep learning-based
automatic approaches [CIdC∗ 17, LZPJ17, ZKPL18, FPA∗ 20].
Figure 6: EM slice (left) and dense segmentation overlay (right).
successive steps, the extracted SIFT features are shown to the user
for debugging by overlaying them on top of the original image.
Once registered, users can visualize and identify incorrect registrations by scrolling through a 2D representation of the stack. In
case of a failed registration, the tool visualizes the displacement ofblocks using color-coded arrows representing vector fields basedon the direction of each displacement according to its angle. Dueto the uniqueness of each dataset, debugging the misalignments isdesigned to be a manual process.
Lindow et al. [LBD∗ 21] have developed a software-assisted algorithm for the alignment and matching of filamentous structuresin serial-section EM tomography. It provides visualization and interaction tools for each registration and reconstruction process toguarantee a real-time response. More recently, deep learning techniques are being investigated due to their advantage of improving
performance and improved accuracy and robustness to learn features in the sample for registration [YHT∗ 17, Jai17].

5. Segmentation
Once a 3D volume is recovered from the raw microscopy data, individual neuronal structures need to be segmented to enable the detailed study and visualization of separate structures and their connectivity. A dense segmentation of an EM slice is shown in Figure 6. A full survey of the state of the art in semantic and instancesegmentation of biomedical images is out of scope for this paper.
Therefore, we point the reader to recent surveys on deep learningfor image analysis and segmentation [MBK∗ 19, LRL∗ 21] and focus on approaches targeting connectomics and neuroscience.

5.1. Automatic Segmentation Approaches
While the ultimate goal for segmentation in the connectomics
pipeline is to extract relevant structural and connectivity information from large microscopy volumes, the techniques employedvary based on the imaging scale, image resolution, and modality. Microscale segmentation approaches capture larger structural
shapes in microscopy images, such as cell bodies and neurites. On
the other hand, nanoscale segmentation approaches work on highdetail image cross-sections that include individual cell organelles
and synapses. Both approaches, however, have to overcome data
challenges, such as low signal-to-noise ratio, imaging artifacts,
foreground blurring, or slice mis-alignment [SCJB∗ 21].
Microscale Segmentation. At the microscale, segmentation focuses on tracing the path of a neuronal structure along its centerline and determining its starting point (typically cell bodies) andNanoscale Segmentation. Automatic segmentation methods for
EM data mainly perform dense segmentation (i.e., every pixel in
an image is assigned a label) based on deep learning. Recently
published large EM connectomics datasets [SCJB∗ 21,XJL∗ 20] use
flood filling networks (FFN) [JKL∗ 18] for segmentation. FFNs add
recurrent pathways to convolutional neural networks (CNN), whichhelps maintain the neuron shape prediction over multiple steps ofthe segmentation process. FFN based architectures [JKL∗ 18] outperform other state-of-the-art methods [ZWJ17, LZL∗ 17, FTG∗ 17]
by producing fewer merge and split errors. Since hyper-parameter
configuration, network architecture, and other attributes of thelearning pipeline largely depend on the data size and available
hardware, efforts toward a self-configuring pipeline have been
made [IJK∗ 21]. More recently, Lin et al. [LWLP21] have developed an open-source deep-learning framework for the semantic
and instance segmentation of volumetric microscopy images built
on PyTorch. 3D segmentation also comes with the challenge of
tracking 2D slice segmentations through the imaging stack. Crossclassification clustering (3C) [MMS∗ 19] solves this task specifically for EM connectomics, where multiple complicated, intertwined neurons need to be tracked. All these approaches requirelabeled microscopy imaging data. Lauenburg et al. [LLZ∗ 22] recently presented a method for 3D instance segmentation of unlabeled images from the expansion microscopy domain by leveraging labeled images from the EM domain via a cyclic segmentation GAN (CySGAN). Other segmentation approaches include
local shape descriptors (LSDs) [SND∗ 21], dense voxel embeddings [LLLS21], or FusionNet [QHJ21]. To scale segmentation up
to petascale imaging volumes, Chunkflow [WSLS21] allows distributing computational tasks over the cloud and local resources.
5.2. Interactive Segmentation Approaches
Currently, deep learning is the state-of-the-art method for image
segmentation. However, complex programming setups, imperfectsegmentation results, and limited access to data and computing resources often make it difficult for neuroscientists, primarily nonprogrammers, to take advantage of the recent advances. Therefore,
manual and semi-automatic approaches remain valuable to neuroscientists in practice, as well as interactive methods for fine-tuningof deep learning models.
Model Fine-Tuning for ML-based Segmentation. Haberl etal. [HCT∗ 18] propose a plug-and-play cloud-based image segmentation framework for biomedical images of different imaging
modalities. They build on the DeepEM3D model [ZWJ17], which
was specifically designed for the segmentation of anisotropic EM
imaging volumes. Users can access their service through a website,where they can retrain existing models with their training data or
run DeepEM3D [ZWJ17] on a set of provided data set. The UniEM tool [UBK∗ 19] helps to make automatic deep learning segmen© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

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tation algorithms more accessible through a visual interface, making it usable for non-programmers. Users can generate ground truth
data with a paint tool in Uni-EM and use it for network training.
They also integrated Dojo [HKBR∗ 14] for proofreading segmentation results (see Sec. 6.2). However, due to the design as a desktop
application, users are limited by local hardware and can not accesscloud-computing resources. Ghahremani et al. [GBM∗ 21] propose
a workflow that helps users interactively annotate a small set ofneuronal structures by drawing over 2D cross-sectional views. A
preliminary model is trained using a small annotation set. Consequently, instead of manually generating a sufficiently large datasetof annotations, the preliminary model is used to create draft annotations, which they can fine-tune. The training of the preliminarymodel can also be iterative to reduce the fine-tuning effort.
Semi-automatic Segmentation. Ilastik [SSKH11, BKK∗ 19] is atool for the semi-automatic segmentation of microscopy image volumes. It uses a random forest classifier to infer a segmentationfrom label inputs provided by the user. The features are learned
from 3D neighborhoods of pixels. Users can refine segmentation
outputs interactively and are guided by a segmentation uncertainty
map pointing them to ambiguous segmentation regions. Roberts etal. [RJVR∗ 11] propose another semi-automatic segmentation technique that uses sparse scribbles to generate accurate segmentationin EM image volumes. First, the user scribbles into a neuron’s
cross-section in each volume slice. 2D segmentations for each slice
are then predicted by minimizing a variational segmentation energyfunction. These 2D segmentations are the fixpoints for generatinga 3D volumetric segmentation shape. NeuroTrace [WBH∗ 10] suggest segmentation masks using multiphase level sets [VRMP09]
and integrates a scalable volume viewer. TrakEM [CSS∗ 12] alsooffers semi-automatic reconstruction, synapse labeling, and skeleton extraction. VAST [BSL18] is also a widely used program for
manual and semi-automatic labeling and segmentation of large microscopy image volumes. Users can annotate the volume at different resolution levels to speed up segmentation. By outlining closed
contours of cells, VAST automatically fills the selected region.
Manual Segmentation. Neurolucida [GG90] is an early tool
developed primarily as a manual computer-assisted tracing system for light microscopy systems. The specialty of this software is its integration with the microscopy system to superimpose both image data and system control functions for manual
tracing. Given that Neurolucida is not freely available, alternative manual segmentation tools [PHK04, PLM11, MCVA17] areavailable. 3D Slicer [PHK04] is a widely adopted user interface
integrating ITK segmentation functionalities. Catmaid [SCHT09]
and VikingViewer [AMG∗ 11] are multi-user collaborative environments for manual segmentation of large datasets. Catmaid additionally allows synchronized inspection of multiple registered datasets,such as comparing EM and light microscopy volumes. Scalability in these systems is achieved through a tiled image pyramid,
enabling the browser only to load relevant data pieces into working memory. Many of the manual and semi-automatic segmentation tools also support proofreading, such as VAST [BSL18], Catmaid [SCHT09], and the Viking Viewer [AMG∗ 11]. We outline
their proofreading capabilities in Section 6.
Immersive Neuron Tracing. Studying the complex circuits of neu© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

rons on a 2D computer screen requires projecting their 3D shape
into 2D. Spatial understanding can only be done by interacting withthe data. Virtual reality (VR) provides an immersive viewing andnavigation interface for 3D data, enabling more intuitive interactions with complex spatial structures.
Boges et al. [BCM∗ 19, BAS∗ 20] leverage this advantage by
proposing a new approach to creating, proofreading, and exploring skeletons of neuronal structures in VR. Representing neurons
using skeletons is often more efficient than storing surface meshes
due to reduced data sizes while preserving important topologicalinformation. Their environment is designed to generate skeletonsof neuronal structures represented by surface meshes, quick correction of errors (e.g., branching errors), and efficient quantitative
analysis by measuring distances and topological features. They immerse the user in an endoscopic view inside the neuron. A pathstabilizing feature automatically places skeleton nodes and edges
while navigating through the neuron. Their evaluation reported thatthe path stabilizer could reduce the number of interactions needed
to generate a full skeleton compared to manual skeleton placement.
Usher et al. [UKF∗ 18] present a design study leveraging VR
to trace neurons more effectively. Instead of tracing neurons sliceby slice in the imaging volume, their tool visualizes segmentationsuggestions in 3D. It allows for correcting segmentation merge
and split errors using intuitive 3D navigation. Their method applies state-of-the-art data management and large-scale visualization techniques to support data sizes exceeding RAM capacity enabling the use of consumer-level VR hardware. Users reported that
fine-tuning segmentations is still challenging in VR. Additionally,
the authors observed that tracing errors occurred because users forgot to navigate back to branching points of neurons. McDonald etal. [MUM∗ 21] extend this strategy by adding topological features
for user guidance. Their semi-automatic technique computes multiple potential paths that follow a neuron using the Morse-Smale
complex (MSC) [GBHP08]. Based on this set of paths, users selecta subset that contains the actual path of the neuron. This approachmakes neuron tracing more automatic while preserving the advantages of 3D navigation in VR.
5.3. Connectivity Extraction
EM-based imaging remains the gold standard for connectivity
analysis. In principle, EM allows the identification of individual
synapses, making connectivity analysis more accurate and moredata-intensive. However, detecting synapses in EM data is still asignificant challenge that needs to be tackled to generate reliable
connectivity information. Light microscopy often cannot resolve
individual synapses due to limitations in imaging resolution, giving rise to methods that estimate the connectivity between neurons
based on statistical rules.
Synapse Annotation. Several segmentation and proofreading tools
support the annotation of synapses, where users have to manuallyor semi-automatically set the location of a synapse and mark theirconnecting structures [SCHT09, BSL18, ZOYP18]. Additionally,
several automatic synapse detection methods have been proposed
recently. Huang et al. [HSP18] use a UNET CNN to detect presynaptic structures and a multilayer perceptron (MLP) to find postsynaptic structures. Parag et al. [PBK∗ 18] detect synapse location

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and the direction of connectivity simultaneously. A 3D U-net isused for synapse detection and a 3D CNN for result pruning. Lin
et al. [LWJ∗ 20] employ an active learning approach to suggest themost informative synapse instances in the unlabeled data given an
annotation budget. Other recent automatic synapse detection approaches were proposed by Berning et al. [BBH15], Dorkenwald
et al. [DSK∗ 17], and Buhmann et al. [BSMM∗ 21].
All previously presented methods studying brain connectivity
rely on many intermediate steps to generate connectivity data. Roncal et al. [GRKV∗ 15] present the first end-to-end pipeline thattakes a microscopy image volume as input and outputs a graphof connectivity information. They combine a set of state-of-theart algorithms for graph extraction. Their pipeline starts by detecting the membranes of neuronal structures [CGGS12]. Next,
they create a three dimensional neuron segmentation using Rohana [KVRKB∗ 15], Gala [NIKP∗ 13], or a simple watershed-basedalgorithm [BM00]. This set of segmentation algorithms computes
the nodes (neurons) of the connectivity graph. To find the link
between the nodes (synapses), they use a Random Forrest classifier [RPKF∗ 14] to detect synapses in the data set. To optimize theirresults, they performed a hyperparameter grid search for all usedalgorithms. They evaluated 1, 856 graphs and published the parameter configuration for the best graph obtained when they tested theirpipeline on the Kasthuri dataset [KHB∗ 15] (see Table 8.2 for a listof open-source datasets). All parts of the presented pipeline introduce errors that propagate throughout the process. Especially, small
spine segmentation errors can lead to larger inaccuracies in the connectivity graphs. To quantify these errors, Roncal et al. [GRKV∗ 15]
present two graph quality assessment measures based on the dualgraph, called the line graph. Synapses are represented as nodes
in the line graph, and neurons indicate edges. First, the Frobenius
norm [GVL96] is used to measure the error between the line graphof the ground truth and the line graph of the estimated graph. Thismeasure, however, is unbound and increases with the graph size,
which can result in misleading interpretations. The second quality
metric leverages the F1 score between the edges of the estimatedline graph and the edges of the ground truth line graph. Using thismethod, true positive edges are represented in the estimated andground-truth graphs. False positives are only shown in the estimated graph, and false negatives are missing in the estimated graph.
Approximate Connectivity Prediction. In some cases, the resolution of imaging techniques is too low to resolve individual synapses
between neurons, such as in light microscopy. In that case, it isharder to make reliable statements about neuron connectivity. Scientists use Peters’ rule [RMA17] to predict connections. This rulestates that proximity between neurons increases their chance offorming synapses. Swoboda et al. [SMB∗ 14, SMB∗ 17] build on
this assumption by proposing a tool that studies connectivity byvisually analyzing neuron arborization overlaps. Since displaying
all branches of the overlap directly in 3D would lead to a clutteredvisualization, they only render glyphs (i.e., small dots color-coding
the amount of overlap). Hovering over the glyphs reveals more information about the particular overlap. Arborization overlaps between neurons are computed upon startup of the application withina few seconds. Other research has found that Peters’ rule is not accurate and, therefore, proximity between neurons is not enough topredict synaptic connectivity [KHB∗ 15].

Motif Discovery. One way to extract information from large
connectivity graphs is to search for common motifs inthem [UHM∗ 21]. A graph motif is a recurrent and statistically significant subgraph or pattern of a larger graph and, applied to brain
circuits, could give insights into the biological principles of thebrain. Optimally, motif discovery techniques should be performed
on dense brain tissue reconstructions to ensure that all connections
between cells are considered.
Motif discovery algorithms can be categorized in networkcentric and motif-centric approaches [RPS∗ 19]. Network-centric
approaches use subgraph enumeration techniques such as the
Kavosh algorithm [KAE∗ 09] to list all subgraphs of a certain sizein a graph. This approach is computationally expensive but allows
identifying common subgraphs without having a prior hypothesis. On the other hand, motif-centric approaches such as DotMotif [MRJ∗ 21] require the user to sketch a specific motif first. Thenthe algorithm searches the graph for instances of that motif. We refer the reader to detailed surveys on graph theory, motif discovery,and subgraph enumeration techniques [RPS∗ 19, YFZ∗ 20].
Song et al. [SSR∗ 05] were among the first to study small motifs in the visual cortex of a rat. Specifically, their network-centric
approach investigates how random graphs differ from biologicalcircuits. They apply a statistical motif detection algorithm wherea motif is found if its count is higher than the expected count ina random graph. They identify small motifs such as many bidirectional connections in two-neuron circuits. Furthermore, they found
an overrepresentation of triangular patterns in three-neuron circuits.
Neuroscientists believe that some psychiatric diseases originate
in specific brain wiring patterns [LLS08, BB09]. Vogelstein etal. [VRVP13] propose a method that can help identify these malfunctioning connections and study them in more detail. They study
graph classification theoretically and apply their findings to connectomics. Given a set of graphs and their labels, they want to predict a classification for a new unlabeled label graph. By finding socalled signal-subgraphs, their method can identify the differences
between two graphs by highlighting unique edges. This method enables the comparison of invertebrate brain graphs of different individuals of the same species, such as the C. elegans.
Ideally, motif discovery is performed on large subsets of thebrain. In that way, motifs spanning different areas of the brain
can be identified, and neurons do not get truncated. Scheffer et
al. [SXJ∗ 20] present a study of a large portion of the brain of afruit fly. They study both big and small motifs. They found overrepresented reciprocal connections in small motifs and cliques (i.e.,graph structures where every node is connected to every other node)as the most common large motifs.

6. Proofreading
Reconstructing a connectome from high-resolution EM images ofbrain tissue requires extensive human effort to proofread automatically generated segmentation and synapses. Current automatic segmentation algorithms can handle large and complex datasets thatwould be infeasible to segment manually with relatively high accuracy. However, these automated methods are still less accurate than© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

manual expert segmentations. Therefore, during proofreading, scientists identify and correct errors in the automatic reconstructionof their data, a requirement for subsequently extracting the wiring
diagram. Even a few incorrect labels or missing synapses could
otherwise change the connectivity diagram significantly.
The proofreading workflow can roughly be categorized into threesteps [HKBR∗ 14]: First, searching and identifying structures that
contain segmentation errors or missing segmentation; second, modifying and correcting the existing segmentation; and third, confirming and verifying the correctness of the modified segmentation.
The most common errors in automatic segmentation data in connectomics are merge errors, split errors, and missing synapse labels. Merge errors occur when two separate structures are incorrectly connected and merged into a single segment (i.e., undersegmentation). Split errors occur when a single structure is incorrectly split into two separate segments (i.e., over-segmentation).
Split errors are typically faster to correct than merge errors, as only
the look-up table from segment labels to actual biological structures needs to be updated so that two segment labels correspondto a single biological structure. Therefore, many automatic segmentation algorithms are tuned to slightly over-segment the data(rather than under-segment) to speed up subsequent proofreading.
In addition to split- and merge errors, the third type of errors are
missing or incorrect synapse annotations. This error occurs whensynapses are either not detected at all or marked at an incorrect position. Synapse annotations are typically not part of the segmented
data but stored as tabular metadata, including the synapse’s spatial
position, the two structures it connects, and optional data such asinferred synapse strength.
Some proofreading approaches only support the correction ofmerge and split errors, while other tools focus more on correcting the connectivity based on synapse annotations. In the following, we outline current proofreading approaches, starting withpurely manual methods (Sec. 6.1), before outlining semi-automatic(Sec. 6.2) and automatic proofreading approaches (Sec. 6.3), as
well as crowdsourced proofreading (Sec. 6.4).
6.1. Manual Proofreading
Manual proofreading tools allow users to change incorrect segmentations. However, they do not offer any automatic support or
guidance. This means that all manual segmentation tools discussed
in Section 5.2 that allow the user to draw on individual image
slices to segment structures in the image can also be used for
proofreading (e.g., Catmaid [SCHT09], Viking Viewer [AMG∗ 11],
WebKnossos [BBB∗ 17], and VAST [BSL18]). In the following, we
focus explicitly on their proofreading capabilities. A downside ofpurely manual approaches is that they neither support the user infinding errors nor do they provide methods to fix erroneous segmentation with minimal user input semi-automatically.
VAST [BSL18] is a widely used segmentation and proofreading
tool for EM data that does pixel-per-pixel segmentations. It allows
users to draw segmentation masks on top of the displayed slice image and, more recently, even offers support for fixing split- andmerge errors. VAST is a single-user tool that focuses on voxel-based
segmentation masks and manual painting.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 7: The skeleton tracing interface of Catmaid [SCHT09] includes a slice view for adding skeleton elements, a 3D viewer, andan abstract connectivity view.

Instead of working on voxel-based dense segmentations, where
every voxel is assigned to a label, several tools focus onquickly reconstructing and proofreading connectivity data. Catmaid [SCHT09] is an open-source web-based tool for terabytescale image data browsing that also supports collaborative microcircuit reconstruction and annotation. Catmaid’s approach focuseson letting users trace skeletons of neurons instead of doing a fullpixel-per-pixel segmentation (see Fig. 7). It supports synapse annotation to reconstruct neuronal connectivity and offers several graphviews. The Viking Viewer [AMG∗ 11] is similarly a collaborative
annotation environment for extracting skeletons in terabyte-sizeddatasets. It supports multi-channel data and creates graphs to display neuronal circuits.
Boges et al. [BCM∗ 19, BAS∗ 20] focus on skeleton-based proofreading in immersive and virtual environments. Users can create or proofread skeletons that were automatically generated by
the Mean Curvature Flow (MCS) [TAOZ12] and Centerline Tree
(CLT) [SBB∗ 00] algorithm. Proofreading tasks include identifying
the main skeleton branch, removing duplicate nodes in highly detailed skeletons, and deleting branching artifacts in the soma area.

6.2. Semi-automatic Proofreading
Semi-automatic proofreading aims to minimize required user input
and to speed up and simplify the proofreading process. Most methods focus on the second part of the proofreading workflow, whereusers correct and adjust merge and split errors (after the errors havebeen identified).
Mojo [KBRK∗ 13] is one of the earlier proofreading tools forlarge EM datasets that supports the fast correction of split andmerge errors. Mojo was superseded by Dojo [HKBR∗ 14], which
also supports the fast correction of split and merge errors but alsooffers a web-based multi-user interface to scale up the proofreading
process. Dojo focuses on simplifying the required user interactions.
Split errors can be corrected by simply clicking on the segmentsthat should be merged in the slice view. To correct merge errors,
users have to draw the boundary line between the two structures,

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to the user as a simple binary decision to accept or reject a correction. In a user study, this guided proofreading approach performed
better than focused proofreading [Pla16] and semi-automatic proofreading in Dojo [HKBR∗ 14].

Figure 8: NeuTu [ZOYP18] supports the correction of split andmerge errors in 2D, while showing an overview of the edits in 3D.

and Dojo automatically splits the segmentation into two separate
segments by computing a watershed algorithm.
Several tools have been introduced that combine scalable visualization, segmentation, and proofreading. WebKnossos [BBB∗ 17]
is a web-based framework for visualizing, segmenting, and proofreading large 3D volumes. The tool includes manual brushingand painting and a merger-mode to correct over-segmentation.
It supports voxel-based segmentation as well as skeletons andincludes task- and project management features akin to Neuroblocks [AABH∗ 16] (see Sec. 6.5). WebKnossos also offers
paid services for annotation, segmentation, and dataset alignment.
NeuTu [ZOYP18] is an open-source multi-user tool for neuron reconstruction, proofreading, and visualization. NeuTu supports thecorrection of split and merge errors and the annotation of synapses(see Fig. 8). They use a distributed, versioned, image-oriented dataservice (DVID) [KP19] for fast data access and simultaneous proofreading of multiple users on the same dataset.
6.3. Guided & Automatic Proofreading
In addition to semi-automatic proofreading methods, some guided
and automatic methods have recently been proposed. Guided methods identify where segmentation errors are most likely, and help theuser find and identify errors quickly. Automatic methods then compute corrections for the identified errors.
Sicat et al. [SHM13] use auto-corrections combined with manual
proofreading. The method takes a graph-based approach to compute the midpoints of each segment in each slice and visualize how
the midpoint locations propagate through the volume. Spatial outliers are marked as errors and corrected via automatic interpolation and manual proofreading. This simple approach works wellfor some merge errors but does not account for split errors. For split
errors, focused proofreading [Pla16] uses a region adjacency graphacross slices to compute affinity scores, leading to a list of potential
split errors that can be presented to the proofreader. It builds upon
NeuroProof [Far20] as its agglomerator and is open source.
Haehn et al. [HKT∗ 18] identify split and merge errors by using a classifier based on a traditional CNN architecture. However,they additionally use a suggestion system to speed up and simplify
proofreading. The system loops over potential merge and split error
regions that were detected by the classifier. Every error is presented

As opposed to the above approaches that focus on automatic
proofreading of voxel-based segmentation, the recently proposed
VICE system [GWB∗ 21] aims to correct connectivity-related errors. VICE helps users recover connectivity pathways by focusing
on proofreading local circuits of individual cells. The method uses
heuristics derived from actual proofreading scenarios to compute
likely connectivity errors by considering predicted synapse locations and presenting the detected error locations to the user in ascalable 3D view. The user can then manually correct these errors.
RLCorrector [NJJ21] takes a completely automated approach to
proofreading based on reinforcement learning. The tool can efficiently manage a combination of merge and split errors concurrently by modeling the human decision process in proofreading using a reinforcement agent and combining multiple reinforcement
learning agents in a hierarchical manner. Quantitative results indicate that RLCorrector is better at correcting split errors than theguided proofreading approach by Haehn et al. [NJJ21], but thatHaehn et al. is better at correcting merge errors.
Summarizing, fully automatic proofreading approaches relyon effective automatic error detection and subsequent correction. Several automatic error detection methods have been published in recent years, however, many of them still need to
be integrated into larger proofreading frameworks. Current error detection methods rely either on predictions from image
data [HKT∗ 18, ZTLS17, RMP∗ 17], skeletonization and graphbased methods [SHM13,Pla16,DPM∗ 18], or biological constraints
and heuristics [GWB∗ 21, MHZ∗ 19].
6.4. Crowdsourced Proofreading
Even with semi-automatic and guided methods, proofreading requires extensive human effort for today’s large and high-resolution
EM datasets. Crowd-sourced approaches aim to mobilize gamersand citizen scientists worldwide to proofread large datasets. Eyewire [Seu12b] was the first crowd-sourced gamified proofreading
system. Users are tasked with creating 3D reconstructions of neurons by improving an initial segmentation generated by a deep convolutional network. Users trace each neuron and rack up points
based on speed, skill, and accuracy. A leader board compares activities between users and encourages them to compete with othercommunity members. The platform also provides an interactive
tutorial to make the onboarding of new users easier. It attracted
over 200,000 gamers and led to follow-up studies that investigatedplayer motivation for participating in a community-based science
project [TLRSH17].
FlyWire [DMM∗ 22] is a similar crowd-sourced platform thataims to create an open community dedicated to proofreading theneural circuits in a Drosophila melanogaster (i.e., fruit fly) brain(see Fig. 9). However, compared to Eyewire, which targets nonexperts and the general public, FlyWire is geared towards a slightly
more experienced and knowledgeable audience. Both Eyewire and
FlyWire provide extensive training material for onboarding new© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

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specifically designed for organizing heterogeneous image stacks,
segmentation data, and metadata for fast data access (Sec. 7.1).
We then describe methods for spatial exploration (Sec. 7.2) andconnectivity exploration (Sec. 7.3). We categorize spatial data exploration approaches based on segmentation availability and how
the segmented data is represented, either as geometry or as volumedata. In Sec. 7.4 we describe interactive visualization-based analysis approaches, including neuron shape analysis, spatial neighborhood analysis, as well as visual query systems that combine spatial
and connectivity analysis. Finally, we describe current visualization approaches for connectomics that focus on communication andpresentation purposes (Sec. 7.5).
Figure 9: Flywire [DMM∗ 22] is a crowd-sourced and gamified
proofreading platform for a fruit fly dataset.
users and proofreaders. FlyWire is designed to make edit operations
fast and traceable in a many-user environment and ultimately scale
up to whole-brain connectomics. Flywire represents the segmentation as a graph of supervoxels. A supervoxel is an atomic group ofvoxels that is never split. Therefore, merge and split operations canbe represented as adding or removing nodes in that graph.
Similar approaches to crowd-sourcing will be necessary to
proofread recently acquired large human connectome datasets containing more than a hundred million synapses [SCJB∗ 21].
6.5. Segmentation Provenance and Project Tracking
Manual and semi-automatic segmentation approaches and proofreading of automatic segmentations need considerable human effort. Depending on the data size and user experience, it mighttake weeks to months to get to a fully segmented and proofread dataset. Since mapping neuronal circuits is time-consuming,
it is often done collaboratively. Accordingly, tools like NeuroBlocks [AABH∗ 16] help to coordinate these efforts by offering avisualization system for tracking the state, progress, and evolutionof very large volumetric segmentation data in neuroscience. Similarly, WebKnossos [BBB∗ 17], an open-source annotation, proofreading, and visualization tool, incorporates an annotation task
and project management interface and versioning system for usercreated annotations. DVID [KP19] is also a cloud-based versioning system for large-scale connectomics segmentation images. Versioning is particularly important when updating automatic segmentations during proofreading.
7. Visual Exploration & Analysis
Imaging and segmentation of connectomics datasets reveal extremely complicated neuronal shapes and neuronal circuits. Automated analysis methods alone are not enough to capture their fullcomplexity and to translate the data into insights for neuroscientists. Therefore, visualization tools are essential to make sense of
the available information. These tools need to scale to vast amountsof data and, at the same time, find a good balance between enabling
complex analysis while limiting mental load.
In the following, we briefly review essential data structures© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

7.1. Data Structures for Connectomics
Nowadays, some connectomics data sets already exceed petabytes
in size [SCJB∗ 21, YBB∗ 20]. Therefore, visual analysis ultimately
depends on efficient and sophisticated data management methods
to access imaging and connectivity data.
Data Compression and Multi-Resolution Data Structures. For
efficient data storage and access, segmentation volumes can becompressed by exploiting large homogenous segment regions withno natural relationship between segment ids [MHL∗ 17]. Thismethod can reduce data storage of low-frequency segmentation
volumes by a factor of 600-2,200x. In addition, efficient rendering of meshes and volumes depends on multi-resolution data structures that reduce the required GPU memory and allow for adaptive resolution changes based on the viewers’ distance. For that,either mesh compression techniques [GHS∗ 22], multi-resolution
data structures [DFM02, HBWP12] or adaptive rendering methods
can be used [GCBM∗ 17].
Connectivity Data. Large connectivity graphs often exceed
the size of consumer hardware working memory. Connectivity analysis tools like NeuPrint [CDU∗ 20] (see Sec.7.4.3) build
on existing technologies like neo4j’s graph querying language
Cypher [FGG∗ 18], for analyzing neuronal connectivity graphs.
Custom data structures for combining connectomics connectivity
data with other data modalities have also been proposed. Ganglberger et al. [GKHB20] present a data structure for querying large-scale connectivity matrices. They store voxel-wise connectivity in a spatially organized way using space-filling Hilbert
curves [Hil35]. Voxel-wise connectivity represents a single voxel’s
cumulative connectivity with a volume of interest (VOI). Additionally, connectivity matrices are stored at multiple resolutions. For
instance, a single matrix element in a four times downsampled connectivity matrix corresponds to four voxels in the image volume.
Connectivity matrices can also be aggregated by brain regions sothat one element in the matrix corresponds to all voxels of a brain
parcellation. The matrix can be further compressed by only taking
non-zero values into account, exploiting sparseness.
Cloud-based Data Stores. The open connectome project(OCP) [BKK∗ 13] provides a scalable database cluster for the analysis of large connectome data sets that is widely used in theconnectomics community. The database supports the storage oflarge-scale imaging volumes, spatially registered image annotations, and metadata. Scalability is achieved by partitioning the volume into cuboids, which are processed on multiple physical nodes.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Figure 10: Immersive visualization of a large wide field microscopevolume [BJA∗ 19]. © 2018 IEEE. Reprinted, with permission.
Morton-order space-filling curves assign a unique index to eachcuboid to ensure similar indices for contiguous regions. Additionally, they store a multi-resolution hierarchy for each volume. The
current level of the resolution hierarchy determines the size of the
cuboids and the cluster workload. OCP offers an interface to thedata through a RESTful API, making the data accessible throughweb services. GET-requests can extract an arbitrary subvolume
from the database and download it to the client for local analysis. The OCP cluster also supports simple metadata queries andis widely used as the data backend in different connectomics applications [VPF∗ 18]. The brain observatory storage service anddatabase (BossDB) [HKG∗ 22] is a newer generation cloud-based
data access system for petascale neuroscience imaging datasets.
Their spatial database implements a storage hierarchy that manages data transfer between affordable, persistent data storage and an
in-memory data storage like Redis. Finally, many modern connectomics research projects [VMH∗ 16, SXJ∗ 20, CBB∗ 21, SCJB∗ 21]
opt to store their large datasets in established cloud services such
as Google Cloud Storage or Amazon Webservices. For example,
CloudVolume [SCK∗ 21] is a serverless client python interface thatenables random access to precomputed arbitrarily large image volumes, segmentations, and mesh data.
7.2. Spatial Exploration
Before performing a detailed analysis, scientists often want to get
an overview of the available imaging and segmentation data andthe structures they contain. This includes looking at slices (crosssections) or volume visualizations of unsegmented data, or viewing
the segmented structures in 3D using either surface meshes or voxel
segmentation data. To help users understand the complex spatial
shapes of neurons, 3D visualization is essential. Table 7.2.1 gives asummary of different spatial exploration methods.
7.2.1. Unsegmented Data
After data acquisition and registration, connectomics data are not(yet) segmented. In some cases, segmentations are hard to computedue to a lack of sufficient training data, or alternatively labelingstructures manually would take too much effort. In other cases, asegmentation is not even needed, and experts prefer exploring theraw imaging data. In these cases, spatial exploration tools for unsegmented volume data are used, where volume rendering typically
employs the concept of transfer functions, mapping raw data values
to optical properties in order to distinguish different structures.
Light Microscopy Volume Exploration. FluoRender [WOCH12,

Figure 11: Virtual GPU memory hierarchy [HBWP12] for petascale volume rendering, combining a hierarchy of resolution levels(horizontal axis) with a hierarchy of page tables (vertical axis). ©
2012 IEEE. Reprinted, with permission.
WOH∗ 17] is a framework for visualizing multichannel fluorescence microscopy data that has been used to render neuronal tissue. Structures imaged in up to 100 channels are visualized by
exploiting multi-dimensional transfer functions and sophisticatedcapabilities for channel intermixing. FluoRender also provides thepossibility for users to perform freehand segmentation guided by
the interactive visualization. Boorboor et al. [BJA∗ 19] have introduced a technique to visualize neuronal structures in large light microscopy raw images without the need to apply computationallyand performance-wise expensive image deconvolution processing
algorithms. Moreover, they maximize visual acuity for visualizing
massive brain datasets by deploying their visualizations on an immersive gigapixel-resolution facility [PPKM15] (see Fig. 10).
Large-Scale Volume Visualization. There are several differencesthat distinguish volume visualization approaches for connectomics
EM datasets in comparison to other applications of volume rendering: First, the size of large EM volumes necessitates highly scalable approaches relying on multi-resolution data structures and efficient rendering techniques. Second, EM datasets are often veryanisotropic with the z dimension being at a much lower resolution
than the xy plane, which needs special consideration for efficient
data storage and to reduce artifacts during rendering.
Hadwiger et al. [HBWP12] tackle the size of petascale volumedata via a virtual GPU memory architecture for multi-resolution
volume data. Multiple levels of page tables provide full scalability,
by also virtualizing the page tables themselves, while supporting
output-sensitive multi-resolution rendering via a fully virtualized
3D mipmap structure (see Fig. 11). This basic architecture is more
efficient than standard octrees for highly anisotropic datasets suchas EM data. Further, the authors propose a visualization-driven
pipeline where data loading and rendering is triggered on-the-fly
during the ray-casting pass, depending on the current view. Volume
bricks are only uploaded to the GPU if the ray-caster requests a
missing brick. Furthermore, this approach supports dynamic acquisition of microscope data, as data bricks needed for rendering canbe reconstructed at runtime.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Table 1: Visualization approaches for spatial exploration of connectomics data. We classify tools based on data modality, theirsupport for volume rendering (Vol Ren) and whether they display
segmentation data as volumes (Vol) or meshes (Mesh).

[WOH∗ 17]

Data
LM EM
•

Vol
Ren
•

[BJA∗ 19]••

[PRL∗ 10]••

[GBM∗ 21]••

[WBH14]•••

3D viewer for SWC skeleton files[MSBS∗ 21]

•

•

•

widely used, scalable sliceand 3D mesh view••

analysis and comparisonof pyramidal neurons[TRFE∗ 16]

Segmentation
Vol
Mesh

Notes
general purpose toolenhanced visualization of
WF microscopy images•

early system for neuron
tracinghybrid rendering for uncertainty of segmentation•

handles large anisotropic
data[HBWP12]••[BHAA∗ 13]

•

•

•

scalable volume rendering
for segmented volumes[BAAK∗ 13]

•

•

•

interactive visual queries[HAAB∗ 18]

•

•

•

efficientskipping
structures[BMA∗ 19]•••

scalable culling for segmentation volumes

empty-spacefortubular

In addition to standard GPU-based approaches on workstations
or GPU clusters, recent web technologies are starting to make terascale visualization within web browsers feasible. Usher and Pascucci have shown that, in principle, a high-performance terascale
framework can be built using WebAssembly and WebGPU [UP20].
They combine data streaming and caching, on-the-fly decompression, isosurface extraction from volume data, as well as point cloud
rendering into an interactive tool that is able to tackle terascale data
sets, including connectomics data.
7.2.2. Segmented Data

Figure 12: The depth complexity (number of space skipping operations) of standard octree empty space skipping (left) vs. SparseLeap [HAAB∗ 18] (right). Brighter is slower (more operations). ©
2018 IEEE. Reprinted, with permission.
virtual memory architecture for raw (unsegmented) petascale volume data [HBWP12] to include petascale segmentation volumes,
by adding an additional voxel cache for integer segmentation data.
ConnectomeExplorer [BAAK∗ 13] extends the above frameworks for petascale volume visualization [HBWP12, BHAA∗ 13]with additional metadata for neuronal structures, synapses, andconnectivity information. A visual query language then leverages
this metadata for the interactive, semantic specification, and visual
analysis of correlations between structures of interest. More detailsof this visual query interface are given in Section 7.4.3.
More recent works aim to further optimize volume rendering
performance of large segmented EM datasets by optimizing cullingand empty-space skipping strategies. Segmented petascale volumes
often contain millions of segmented structures, typically resulting
from machine learning algorithms. This large number of labels
makes efficient querying and rendering structures of interest challenging. To tackle this problem, Beyer et al. [BMA∗ 19] present
a scalable culling architecture that combines probabilistic and deterministic data structures for efficient hierarchical culling (filtering) of segmented connectomics data sets comprising millions ofsegmented structures. The culling data structure is hierarchical anddata-adaptive, choosing the most suitable approach for data representation locally, depending on actual data characteristics, e.g., thenumber and distribution of integer label values. For fast, outputsensitive culling for visualization, the hierarchical space subdivision also incorporates down-sampled multi-resolution data.

Segmented datasets allow targeted inspection of individual neuronal structures and circuits. Therefore, neuroscientists most commonly segment their data to increase analysis and visualization
quality. Segmented data is typically stored on a per-voxel basis, resulting in an additional segmentation (or label) volume that storesthe segment ID for each voxel. Visualization algorithms can either directly use the labeled voxel data or, alternatively, use precomputed surface meshes of each labeled structure in the segmentation volume. The advantage of voxel-based segmentations is thatthey can be easily integrated into the volume rendering pipeline,
enabling scientists to glance inside segmented structures and e.g.,
see cell organelles within segmented neurons. On the other hand,surface meshes are much more compact to store and more efficient
to render but only give insight into the outside (i.e., the surface) of
a segmented structure. Below, we group different approaches basedon whether they (mainly) work on voxel-based segmentation data,
surface meshes, or use a combination depending on structure type.

In addition to the large number of objects, connectomics datacontain many finely detailed and thin structures, such as axons anddendrites. Rendering these structures fast enough is a tremendous
challenge, because traditional empty space skipping techniques forray-casting incur significant overhead for data lacking large contiguous areas of empty space. To alleviate performance problems
due to a fine-grained fragmentation of space caused by fine structures, the SparseLeap [HAAB∗ 18] method for empty space skipping combines fast GPU rasterization, modern GPU features introduced for order-independent transparency, and volume ray-castingto achieve better scalability for the visualization of highly-detailed,segmented connectomics data (see Fig. 12). SparseLeap interactively adapts to changes in the selected set of neuronal structures,for example due to interactive query evaluation as in the ConnectomeExplorer [BAAK∗ 13] framework.

Volumetric (Voxel) Segmentation Data. To handle large, segmented EM data, Beyer et al. [BHAA∗ 13] extended their previous

In summary, volume rendering methods for segmented EM
datasets all focus on computationally scalable approaches. They

© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

hand, they contribute a usable way to select an x-y-z location directly from the 3D visualization using only a few mouse clicks.
The user must select the desired point in the 3D visualization fromtwo different viewing angles.
More recently, NeuroConstruct [GBM∗ 21] has leveraged a hybrid rendering technique to visualize the uncertainty of CNN segmentation results in light microscopy images. It renders areas ofhigh segmentation confidence as isosurfaces, while uncertain segmentations are visualized by direct volume rendering.

Figure 13: WebGL-based rendering in Neuroglancer [MSBS∗ 21]of the FlyEM hemibrain dataset [SXJ∗ 20] showing an EM slice
and three out of 25,000 segmented neurons.

build on multi-resolution data structures and visualization-driven
pipelines and techniques such as efficient culling and empty spaceskipping to achieve interactive framerates for rendering datasets
containing millions of finely segmented objects.
Mesh Segmentation Data. Displaying segmented connectomics
structures as surface meshes is often coupled with slice views of
the raw image data, to give users a better context of the data. Neuroglancer, for example, is an open-source, web-based viewer forvolumetric microscopy and segmentation data built upon WebGL(see Fig. 13). A tile-based architecture supports multi-scale visualization of petascale imaging and segmentation volumes, making it
one of the most scalable web-based viewers for connectomics data.
Large data sets and segmentation volumes can be stored in cloudservices such as Google Cloud Storage. The Neuroglancer client
subsequently downloads small chunks of the data set for visualization. Neuroglancer overlays cross-sectional views of microscopy
images with multi-scale 3D surface meshes and skeletons. It is currently one of the most widely used visualization software for large
connectomics data sets. However, Neuroglancer is designed purely
for data viewing and currently does not support any analysis functionality natively. Sharkviewer [WBH14] is a minimalistic, webbased interactive visualization library to visualize neuronal skeletons. It is used as a plugin for NeuPrint [CDU∗ 20]. Segmentation
meshes are the data type of choice for many visual analysis tools
in connectomics, as meshes are smaller and easier to handle thansegmentation volumes. We will describe these visual analysis tools
and their visualization capabilities in more detail in Sec. 7.4.
Mesh and Volumetric Segmentation Data. Several approaches
use both, a volumetric and mesh representation of segmentation
data, mainly for light microscopy data. V3D [PRL∗ 10] was oneof the first frameworks for the visualization of gigabyte-scale lightmicroscopy image stacks. It is designed as an extensible software
framework, that can be adapted to address different biological problems. The paper demonstrates the configuration of a V3D plugin
designed to reconstruct neurons. This work was one of the first visual analysis tools for connectomics data that implicitly addressedscalability to data size. They render a lower-resolution representation of the volume once the user interacts with the 3D navigation.
Their approach is still limited by the memory size of the GPU but
already adds features to increase rendering efficiency. On the other7.3. Connectivity Exploration
Since the ultimate goal of connectomics is to reconstruct the complete wiring diagram of a brain to map structure to function, it isessential to provide visual tools for analyzing connectivity information. Connectivity can be studied at multiple levels of detail andin many different ways. Many visualization approaches display aconnectivity graph as a node-link diagram where information about
neuron shape is lost (e.g., [SCJB∗ 21]), while other approaches stilltake the morphology of neurons into account (e.g., [AABS∗ 14]). In
this section, we focus on visualization methods for neuronal connectivity graphs. We summarize the different methods in Table 7.3.
Node-Link Views. The most straightforward approach to visualizing the reconstructed connectivity is to show the resulting graph.
In a neuronal connectivity graph, neurons are represented as nodes,
and synapses are abstracted to edges between nodes. Abstracting
the imaging data into graphs helps reduce the size of tera- andpetabyte data sets. However, any information about cell morphology and spatial relations is lost.
Several tools support node-link diagrams for displaying neuronalconnectivity information (e.g., [BAAK∗ 13, AMG∗ 11, SCHT09].
However, these graphs quickly get visually overloaded when thedata size increases. Recently, Shapson-Coe et al. [SCJB∗ 21] haveimaged one cubic millimeter of human brain tissue at nanoscale
resolution, leading to 1.4 petabytes of imaging data. They render
the connectivity of specific cells as a simple node-link diagram.
The displayed graph is extremely dense, and efficient connectivity analysis remains an open problem at this scale. In addition, humans have proofread only a small subset of neurons, and, therefore,
connectivity errors remain. This shows the importance of effective
crowd-sourced proofreading (Sec. 6.4).
Many approaches studying neuron connectivity are limited tosmall but densely segmented subvolumes of the brain. This limits the analysis by considering incomplete reconstructions of neurons that were truncated by the bounds of the volume. Kunst etal. [KLM∗ 19] present the first brain-wide wiring diagram at cellular resolution for vertebrates. Specifically, they acquired over 2, 000
sparsely individually labeled neurons and gene expression data in areference atlas of zebrafish larvae brains. They visualize neuronalconnectivity with node-link diagrams and an adjacency matrix. It
incorporates features to search for morphologically similar neurons
using NBLAST [CMO∗ 16] (see Sec. 7.4.1), query neurons based on
their trajectory through brain regions, and access metadata of individual neurons.
Other connectivity analysis approaches include diverse imaging© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Table 2: Connectivity visualization approaches. We classify tools
based on data modality, visualization method, and whether the visualization displays morphological (i.e., structural) information forneurons (Morph. shown). Note, that many tools for neuron tracing
or segmentation also include node-link diagrams, but have not been
included in this table for brevity.
Data
LM EM

Vis Method

Morph.
shown

Notes

[GSF∗ 19]•

node-link[SBS∗ 13]•circuit wiring diagram[MPL∗ 17]•

mosaic matrices,mirrorglyphs,
Kiviat diagrams

temporal connectivity visualization

connectivity queries
in multimodal data•

connections based onarborization overlap[BAAK∗ 13]•

node-linkinteractive (connectivity) queries[SCJB∗ 21]•

node-linkconnectivity graph of
1 cubic milimeter ofhuman brain tissue[KLM∗ 19]•

node-link,adjaceny matrix

inter-region connectivity matrix withclustering of similar
regions.

[CDU∗ 20]•

adjacency matrix

adj. matrix between
brain regions[AABS∗ 14]•

2D subway line
metaphor•

multi-scale connectivity exploration andvisualization

modalities to augment connectivity information. For example, spatial gene expression shows the influence of particular genes on specific brain regions. To study the correlations between gene expression and neuron connectivity, Ganglberger et al. [GSF∗ 19] developed BrainTrawler. This interactive web application allows querying and analyzing connectivity and gene expression data. Combining these with heterogeneous data sources helps correlate spatial
gene expressions to local connectivity patterns. BrainTrawler organizes connectivity information at different levels of scale. Whileneuron-based connectivity helps to understand circuits at the lowest level, analyzing connectivity between brain regions is also supported. A custom data structure [GKHB20] maintains interactivity
and fast querying of multiscale and multimodal data (see Sec. 7.1).
Abstract Connectivity Views. In contrast to standard node-link
views, several visualization approaches have looked at brain connectivity from different angles.
For instance, Neurolines [AABS∗ 14] uses a subway map
metaphor to simplify connectivity analysis in the connectome (seeFig. 14). The complex shapes of neurons make connectivity analysis in 3D complicated due to visual clutter. Therefore, Neurolines
projects the 3D skeleton of neurons into 2D while preserving neuron topology and relative distances. Hovering over synapses on theskeleton reveals additional information, such as a view of the original EM data and the abstracted skeleton of the connecting neuron.
To enable a scalable analysis of connectivity in large brain volumes,
Neurolines offers multiple levels of abstraction in different views.
Based on confocal light microscopy images, NeuroMap [SBS∗ 13] has employed circuit wiring diagrams torepresent all possible connections of neurons in the fruit fly(Drosophila melanogaster) brain. The more overlap the arboriza© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 14: Neurolines [AABS∗ 14] projects the complex topology ofneurons and their connections to 2D using a subway map metaphor.
© 2014 IEEE. Reprinted, with permission.
tions of two neurons have, the more likely it is that they form asynapse between them [BS13]. Neuromap abstracts the different
elements of neurons, such as the cell body, arborizations, and projections, into a graph structure. This graph is then used to visualize
the overlap of arborizations in a modified node-link diagram, which
is further augmented by highlighting different brain regions andallowing details on demand. RemBrain [MPL∗ 17] also operates onlight microscopy data but tracks temporal connectivity by usingmosaic matrices, Kiviat diagrams, and glyphs. RemBrain is basedon the previous visualization method Swordplots [MFL∗ 16].
Finally, matrix views showing the connectivity between brain
regions are heavily popular in macro-scale connectomics. For
nanoscale EM data, NeuPrint [CDU∗ 20] employs a matrix viewfor visualizing the connectivity between brain regions in the fruitfly brain. Generally, matrix views and abstract connectivity views
scale better to large graphs than node-link diagrams. However, interactive querying and analysis tools for large graphs are essential
to explore and understand the data in detail, which will be discussed
in the following section.
7.4. Visualization-Focused Analysis
One of the final steps in the connectomics pipeline consists of thevisual and quantitative analysis of the initially explored data. This
step relies on the combined spatial data analysis in correlation toconnectivity data. Furthermore, this step requires interactive means
for querying the data to enable neuroscientists to generate and test
hypotheses in petabyte-scale data more efficiently. We summarize
methods for visualization-focused analysis in Table 7.4.1.
7.4.1. Neuron Analysis
Neuron Quantification. In addition to displaying the raw spatial
data, some tools augment visualizations with numerical features
for a more quantitative analysis of the complicated 3D structureof neurons. Several proposed works quantify neurons and neuronalshapes or compute pairwise comparisons between neurons.
For instance, Al-Thelaya et al. [ATAG∗ 21] use a shape descriptor to quantify and classify 2D/3D nuclei shapes. Their descriptor
measures the discrete curvature along closed and resampled contours of the shape. In addition, it is invariant to translation, rotation, and parameterization. They use 3D meshes of brain cell nuclei to compute a feature vector to evaluate the performance andtrain a support vector machine (SVM) using radial basis functions
to predict cell types. They found that neuron nuclei form different

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

content-based information retrieval system (CBIR). A signature
is computed based on a user-defined data selection and used tocompare different data selections. For instance, a neuron signature
is a numerical score based on spine attributes. This helps to querythe data set for similar pyramidal neurons.

Figure 15: 1D "barcode" visualization of the hierarchical tree layout of neurons [CFBH10]. Black segments refer to terminal segments, while green segments start and end at a branching point.
clusters per cortical layer, making it hard to generalize cell typeclassification for all neurons.
Instead of only characterizing soma, NBLAST [CMO∗ 16] is atechnique for the quantitative pairwise comparison of full neurons.
It uses spatial location and branching patterns to compute a similarity score. NBLAST divides the query and target neuron into
small segments, each storing a spatial location and a tangent vector. For each segment, the nearest neighboring segment is found(using the Euclidean distance), and their tangent vectors are compared. NBLAST uses both euclidean distance and the dot productof the tangent vectors to compute the similarity score. NBLAST
can identify biologically meaningful neuronal clusters among different data modalities and has been used to identify the same neuron in light microscopy and EM datasets. Furthermore, a similarity score can be computed efficiently on consumer-level hardware
without training time. Schubert et al. [SDJ∗ 19] use cellular morphology networks (CMNs) for the automatic quantification of neuronal shapes. Their method creates morphology embeddings (Neuron2Vec), which were used to detect glia cells in high-resolution
EM data and to identify segmentation errors.
Interactive Neuron Analysis & Visualization. While the above
approaches for neuron quantification aim to automatically provide
results such as similarity scores or cell type predictions, a different,
more user-centric approach is followed by interactive, visual neuron analysis tools. Recently, ZeVis [CHM∗ 21] has made the interactive visual analysis of cell nuclei morphology and their distributions
possible. EM segmentations of nuclei can be compared interactively and filtered by brain region or specific shape attributes. Theyvisualize nuclei distributions using 3D histograms and their locations using 2D multi-axis views. For the purpose of nuclei analysis
and model training, Lin et al. [LWP∗ 21] and Mu et al. [MYT∗ 21]
provide a dataset containing around 170, 000 nuclei segmentations.
Instead of the analysis of cell nuclei, PyramidalExplorer [TRFE∗ 16] focuses on the visual analysis of pyramidal
neurons, a specific neuron type. Pyramidal neurons [DF92] exhibit
long branches spanning multiple cortical areas. PyramidalExplorer
not only has data viewing capabilities but also supports morphological data analysis. For instance, numerous quantitative attributes
for each spine are extracted, such as volume, surface area, position,and maximal diameter. Spines can be color-coded according toselected morphological features. They can also be inspected indetail in a separate 3D view. PyramidalExplorer implements a

In comparison, Pastor et al. [PBB∗ 21] use more high-level andnatural features to quantify the spatial shapes of neurons. Theybuild on a set of existing tools [PMT∗ 15, GCBM∗ 17, TRFE∗ 16]
to develop a unified framework for morphological neuron analysis
at different levels of abstraction. Their framework provides highlevel abstract representations of the trajectory of neurons throughdifferent cortical layers. When studying neuronal morphology ingreater detail, the tool allows for comparing quantitative featuresof small neuronal structures, such as dendritic spines. For instance,
users can visualize juxtaposed 3D models of spines and sort thembased on spine length, volume, surface area, or any other featurethat has been extracted from the data in a preprocessing step. In
comparison, NeuroMorphoVis [AHE∗ 18] supports a broader set oftasks from morphological skeleton and soma analysis to neuron
data handling and generation of high-quality renderings. Cuntz etal. [CFBH10] propose a method to summarize the neuronal branching pattern in a 1D "barcode" visualization (see Fig. 15). They use
the hierarchical tree structure of neurons and identify all branching and terminal points of the skeleton. Each segment of the tree isvisualized as one section in the barcode. The length of the section
directly relates to the length of the segment in the skeleton. Greensections start and end at branching points, and the black sections
indicate a terminal segment. The neuron analysis & visualization(Navis) python library [SBJ∗ 21] build ontop of natverse [BMJ∗ 20]
is designed to analyze and visualize neuron morphology. It implements state-of-the-art algorithms such as pairwise neuron comparison with NBLAST [CMO∗ 16], neuron simplification tools, and advanced neuron plotting algorithms. By labeling branches in the neuron trees by their distances to the soma, simplified 2D versions ofneurons skeletons are visualized. Navis functionality is based on aninterface for programmatic access to the FlyEM [XJL∗ 20] data set.
Neuron skeletons used in Navis can be generated using the kimimaro python library [SBLW21]. Kimimaro rapidly generates skeletons from dense segmentation volumes. It implements a TEASARderived algorithm [SBB∗ 00]. Neuromorph [JNC∗ 15, JBK18] also
supports the morphological analysis of neurons, but instead of aprogrammatic python interface, it is integrated into the 3D modeling software Blender. Therefore, users have a visual interface forextracting distances or labeling parts of neurons such as spines.
While previous approaches [SBJ∗ 21, CMO∗ 16, SCK∗ 21,
JBK18] focus on specific properties of neuronal morphology,
SNT [AGE∗ 21] generates streamlined connectivity diagrams andcombines multiple quantitative attributes of neurons to study how
they broadcast information between brain regions. For instance,
they extracted the normalized cable length and the number of axonal endings in the MouseLight database [WBF∗ 19] to identify the
number of brain areas innervated by a particular cell.
7.4.2. Neighborhood Analysis
Spatially imaging the brain at a high resolution lets researchersconclude about connectivity between neurons and helps them understand the interactions of neurons with other cell types. This is© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Table 3: Visualization-focused analysis approaches. We classify
tools based on data modality and the type of analysis they support: Neuron Analysis (Neur. A.), Neighborhood Analysis (Nbh. A.)
or Interactive Queries (Qu.).
Data
LM EM

Neur.
A.

[AGE∗ 21]••

quantification
anatomy[AHE∗ 18]••

morphology analysis of neuronal skeletons[TRFE∗ 16]••[BSG∗ 09]•

[ATAG∗ 21]

•

•

•

Shape descriptor for cellular
nucleii[PBB∗ 21]•••

multilevel navigation[CFBH10]•••

1D barcode visualization ofneurons[SBJ∗ 21]•••

[SBLW21]•••neuron skeletonization[CMO 16]••

pairwise neuron similarity
computation without training[SDJ∗ 19]••

neuron morphology embeddings[CHM∗ 21]

•
•
•

•
•

∗

[JBK18][MAAB∗ 18]

Nbh.
A.

•

Qu.

Notes
of

neuronal•

visual analysis of pyramidal
neurons•semantic, spatial & neighborhood queries•

neuron plotting and morphological analysis

cell nuclei analysis••

neuron analysis in Blenderglia and neuron interactions∗[TCG 22]••

scalable comparison of neuronal spatial neighborhoods[ACA∗ 19]••

interactive glycogen absorption analysis[CBH∗ 15]••

immersive analysis of glycogen clusters[ABG∗ 18]••

immersive
analysismetabolic processes[CDU∗ 20]•••interactive queries of the
FlyEM hemibrain[BAAK∗ 13]

•

•

•

query algebra for large EM
data volumesofimportant because support cells and the location and size of cell organelles like mitochondria also influence neuronal function. Most
spatial neighborhood analysis focuses on the relationship between
glia cells and neurons. Glia cells spatially surround and insulate
neurons and support them. Mohammed et al. [MAAB∗ 18] propose Abstractocyte, a visual tool to study the interaction between
astrocytes and neurites in EM volumes interactively. Astrocytes
are special star-shaped glia cells. The tool allows for the interactive abstraction of astrocytes and neurites to simplify their complex shapes. The user interactively sets a point in a 2D abstraction panel to select the abstraction level for neurons and astrocytes.
The horizontal coordinate determines the abstraction of the neurite,
while the vertical axis sets the astrocyte abstraction. In addition,
Abstractocyte allows for spatial queries and clustering of glycogen granules. While Abstractocyte focuses on the visual exploration of interactions between astrocytes and neurites only, Troidl
et al. [TCG∗ 22] propose a more general method to explore spatial
neighborhoods of neuronal structures. Their tool, Barrio, integrates
a scalable approach for comparing multiple spatial neighborhoods
using juxtaposed spatial 3D views and abstract 2D InfoVis views.
Similarly, Agus et al. [ACA∗ 19] study the energy metabolism in© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 16: The NeuPrint web application [CDU∗ 20]. The left column shows neurons with inputs from brain region A and outputs
from brain region B. Two stacked bar charts are displayed for eachneuron, visualizing the distribution of brain regions from the inputs(top chart) and outputs (bottom chart). The right column shows aninteractive 3D rendering of a neuron skeleton.
neuronal tissue using astrocytic glycogen granules, visible in highresolution EM images. They follow a more quantitative approachthan [MAAB∗ 18] and model energy absorption with a glycogenabsorption map. This map stores potentially absorbed energy foreach position in the volume. They augment connectivity information shown as a node-link diagram with the energy absorption map
and incorporate it into a 3D volume rendering view.
Immersive Neighborhood Analysis. Neighborhood analysis also
suffers from limitations of the 2D screen to display complex spatial structures. Therefore, researchers have experimented with immersive technologies to offer a more intuitive perspective on spatial data. Cali et al. [CBH∗ 15] use VR to study the distribution of
glycogen granules inside astrocytes. The cloud of glycogen granules seemed random when looking at conventional 3D renderings
on 2D screens. However, VR helped them to identify clusters in
the distribution of glycogen, which they later could automaticallydetect using the DBSCAN-algorithm [EKS∗ 96]. Based on this observation, they conducted a quantitative analysis of the spatial relationship with the clusters to pre-or postsynaptic elements using the
Neuromorph Blender integration [JNC∗ 15, JBK18].
To study the metabolic support of astrocytic glycogen granules
on neurites in even greater detail, Agus et al. [ABG∗ 18] computea glycogen-derived lactate absorption map (GLAM). The amountof absorbed energy depends on the size of the glycogen granuleand its distance to the neurite. They experiment with displaying theabsorption maps combined with 3D reconstructions of neurites in
a VR environment. In evaluating their method, the authors reportthat users find analyzing GLAM-based visualization in VR at leastas valuable as using a desktop setup. Especially when analyzingcolor-mapped data, users preferred the VR setup over a standard
visualization on a 2D screen.
7.4.3. Visual Query Interfaces
Interactive analysis and query systems are essential for investigating today’s large connectomics datasets. Neuronal datasets andconnectivity graphs are so large and complex that looking at allBeyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

searching neighborhoods of neuronal structures in a region of interest (ROI), topological queries help study neuronal connectivity,
and attribute queries allow searching automatically or manually labeled attributes of neurons, such as the number of spines or thenumber of ventricles in a cell. To efficiently query large volumesof brain tissue interactively, ConnectomeExplorer uses hierarchicaland multi-resolution data structures.
7.5. Visualization for Communication
Figure 17: ConnectomeExplorer [BAAK∗ 13] enables interactive
queries on petascale volumes, using a set-based algebra and hierarchical queries. © 2014 IEEE. Reprinted, with permission.
parts of the data is quickly becoming infeasible. Therefore, scientists need ways to find interesting areas in their data, filter quickly,
and query data to test their hypotheses.
NeuPrint [CDU∗ 20] is an analysis ecosystem that uses existinggraph databases such as neo4j to organize connectivity graph data
at different abstraction levels. NeuPrint tightly integrates an interactive web application for viewing and querying connectomics
data. It shows an adjacency matrix to visualize the connectivity between brain regions. Each element in the matrix shows the numberof connections between brain region A and brain region B. By selecting a matrix element, users see detailed information about theneurons connecting these two brain regions (see Fig. 16). Individual neurons can be selected, and their skeletons are visualized in
3D. Additionally, NeuPrint displays an overview for each neuron,showing from which brain region input neurons (top row) comeand to which brain regions outputs (bottom row) go, as two stacked
bar charts (see Fig. 16). NeuPrint users can also query connectivity
information using the established Cypher language [FGG∗ 18].
In comparison, BrainGazer [BSG∗ 09] focuses more on querying neuronal structures based on spatial features instead of connectivity information. It combines a direct volume rendering (DVR)
approach with three types of visual queries in the fruit fly’s brain.
Semantic queries are initiated by clicking on an object, and if objects are occluded, clicking multiple times flips through all of them.
Object queries allow searching the proximity of neuronal structures
for interesting features. To do so, BrainGazer computes the closest
distances between all objects and allows filtering the neighborhoodof a specific object for other close objects. Finally, path queries
select an arbitrary region in the brain using a freehand drawing selection tool. The selected region, including all selected objects, isloaded into a view. This querying method allows for more accurate
capturing of anatomical areas than rectangular or circular selection
tools. BrainGazer precomputes lookup volumes and distances tables to efficiently access close objects. Data used in BrainGazer
was reconstructed from confocal microscopy images.
ConnectomeExplorer [BAAK∗ 13] combines both spatial andconnectivity queries with a high-performance 3D interactive visualization framework of segmented EM volumes. It implements aquery algebra that allows users to filter and search the data interactively, using efficient hierarchical query evaluation (see Fig. 17).
The tool supports three types of queries: Spatial queries allow

Most visualization tools for connectomics focus on proofreading,
exploration, and analysis of the data. However, initial research has
been done on visualization for communication purposes in connectomics in recent years. Brainrender [CTP∗ 21] is a python-based
open-source interactive 3D viewer for anatomically registered data.
Brainrender aims to generate publication-ready, high-quality illustrative visualizations with minimal programming requirements.
The main goal is to support the visualization of data registered toany anatomical atlas. This is achieved by tightly integrating the toolwith the BrainGlobe Atlas [CPT∗ 20], which provides a unified interface to many publicly available reference atlases. Visualization
with Brainrender is not limited to microscale data of single neurons
but combines data of different scales and imaging modalities.
8. Public Data Sets and Open Source Tools
Connectomics relies heavily on open science [Fre15]. Data acquisition, storage, and processing requires tremendous efforts and resources, which is only feasibly for some larger research labs andtheir industrial partners. Therefore, other researchers depend on
publicly accessible data and tools to test their own hypotheses. Thissection gives an overview of the most relevant data sets and opensource tools developed and maintained by the scientific community.
8.1. Data Sets
We summarize openly available connectomics data sets and their
key characteristics in the following. All datasets are listed in Table 8.2. Furthermore, the Open Connectome Project [BKK∗ 13], theNeuro Data Register [Neu], and BossDB [HKG∗ 22] host a numberof different connectomics datasets, both for light microscopy and
EM. Similarly, the website NeuroMorpho [Uni] hosts digitally reconstructed neurons and glia associated with peer-reviewed publications. Bonney et al. [BCSH∗ 22] recently surveyed publicly available EM datasets to study microvascular structures in the brain.
Light Microscopy. While light microscope data sets require muchless memory space than EM and often do not have extensive segmentation masks, they still provide a valuable resource for neuroscientists. The DIADEM challenge [BBC∗ 11] has produced six
online available light microscope data sets, each imaging a few neurons. They were intended as training data for neuron segmentation
algorithms. Here, we explain two of these data sets in more detail.
The cerebral climbing fiber dataset contains three image stacks ofneurons in a rat brain, and the rat hippocampal CA3 interneuron
data set includes two neurons each in an imaging stack. Weiler
et al. [WCV∗ 14] used fluorescence microscopy to image over six
million cubic microns of cortical mouse brain tissue, stained with© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

ing continuously every year, allowing insights into larger and larger
subvolumes of the brain. Even a full connectome reconstruction ofa fly brain is now within reach [DMM∗ 22].
From here, a plethora of opportunities for novel visualization research arises. Below, we highlight the areas that we think would
benefit most from novel visualization approaches, including scalable and combined proofreading and visual query systems, multiscale connectivity analysis, visual functional pathway exploration,and the visual fusion of multimodal data and living tissue data.
Figure 18: 4k incoming connections on a pyramidal cell in the H01
dataset [SCJB∗ 21] visualized in Neuroglancer [MSBS∗ 21].
synaptic proteins and at synaptic resolution. Bloss et al. [BCK∗ 16]
used both light microscopy and array tomography to image synaptic connectivity between interneurons and pyramidal cell dendrites.
EM. Recently, Shapson-Coe et al. [SCJB∗ 21] released the H01
data set, which covers one cubic millimeter of human brain tissue
imaged with EM (see Fig. 18). The data set is densely segmented,
including approximately 100 proofread cells. With 1.4 petabytes ofdata, it is the largest connectomics data set published so far. Still,
the data set only covers a small fraction of the human brain. Larger
brain volumes are required to fully capture neuronal circuits thatspan over long distances, especially for motif discovery.
The FlyEM hemibrain [XJL∗ 20] data set covers approximately
half of the fruit fly brain and includes segmentations. Comparedto the H01 [SCJB∗ 21] data set, it contains more proofread cells.
Therefore, it is currently considered the data set of choice for motif discovery. Similarly, Zheng et al. [ZLP∗ 18] imaged the wholebrain of the fruit fly with initially only a small subcircuit segmented. However, this data set is currently in the process of being completely segmented and proofread using the crowdsourced
FlyWire platform [DMM∗ 22]. The FANC dataset [PHG∗ 21] is asparse connectome of an adult fruit fly. All neurons and synapses
involved in controlling leg and wing movement have been reconstructed. The MICrONS data set [CBB∗ 21] contains one cubic millimeter of brain tissue of a mouse. It combines EM images withfunctional recordings of visual stimuli of 75,000 neurons, making
it the largest multimodal connectomics data set so far. Its smaller
predecessor data set is also available online [SMBC∗ 20,DTM∗ 21].
In 2015, Kasthuri et al. [KHB∗ 15] collected 660 GB of EM images of a mouse’s neocortex with partial segmentation. Wei and
Lin [WLFB∗ 20, WLL∗ 21, LWP∗ 21] made proofread segmentation
volumes for axons, mitochondria and cell nuclei publicly available.
8.2. Open Source Software Tools
Table 8.2 lists the most relevant open source software tools forconnectomics and also links to related Github repositories, websites and documentation. We focus on open-source and ready-touse tools and software packages.
9. Future Research Directions
Connectomics research is a young subfield of neuroscience currently entering an exciting phase. The size of new data sets is grow© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

9.1. Scalability
Connectomics data sets have increased from terascale to petascale
within the last decade due to advances in image acquisition. Neuroscientists even predict that a full brain connectome of a mousewill likely be reconstructed within a decade [SCJB∗ 21, ABC∗ 20].
Therefore, scalability will remain the main research focus for eachstep in the connectomics pipeline, including visualization.
Many of today’s large data sets are stored remotely on the cloud,and users download only small chunks on demand. However, tosupport interactive web-based visualization and analysis applications, scalable data formats are needed that support compressedstorage and interactive access and provenance tracking of segmentations and concurrent multi-user access. These features will allow
researchers to integrate proofreading and subsequent updating of
the segmentation data into large-scale visual query and analysis
systems and further streamline the connectomics pipeline.
Furthermore, to make the segmentation and proofreading of evenlarger datasets feasible, new approaches are needed that are inherently scalable. Segmentation algorithms will continue to be imperfect, therefore, better human-AI collaboration is the way forward to create usable connectome reconstructions. Dorkenwald etal. [DMM∗ 22] present the first step in this direction, where an automatically generated segmentation is continuously updated throughcrowd-sourced human proofreading. New segmentation algorithms
should also be aware of their limits and forward complex cases
automatically to a human proofreader [JKL∗ 18]. Likewise, visualization research should consider how to design these human-AI
interactions to support the reconstruction of large volumes in a reasonable amount of time.
9.2. Large Connectivity Graph Exploration
Connectomics data allows researchers to evaluate actual wiring diagrams and discover the fundamental principles in neural circuit architectures. However, the community is still in dire need of connectivity analysis methods that explore neuronal connectivity at different scales, ranging from small, local synaptic motifs to higher-level
connectivity patterns involving neurons in several cortical layers.
This should be approached as a multi-scale problem, where different solutions are needed for varying levels of detail in the data.
Tools for motif querying for these data sets are just beginningto be developed [MRJ∗ 21], and currently, all require an intuition
about a particular type of motif. Subgraph enumeration techniques
provide a promising direction to find connectivity motifs without
any prior hypotheses. Visualization research can help find ways toBeyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Table 4: Commonly used and publicly available connectomics data sets.
Name
Climbing Fibers
CA3 interneuron

SampleRat
Rat

Modality
LM
LM

Physical Size
200 µm3
200 µm3#Neurons
3
2

Weiler et al.

Mouse

LM

6 × 107 µm3

not imaged

Bloss et al.

Mouse

1 × 106 µm3

H01

Human

MICrONS 1

Mouse

MICrONS 2

Mouse

Kasthuri et al.
Fly Hemibrain

Mouse
Fruit FlyFAFB

Fruit Fly

Neuromorpho
OpenConnectome
DIADEM

16 species
variousvariousarray tomography
TEM
mSEM
two-photon
calcium imaging &TEM
two-photon
calcium imaging &TEM
SEM
FIB-SEMTEM camera array(TEMCA)variousvariousvarious

MitoEM

Rat & Human

AxonEM
NucMM
FANC

Synapses
not imaged
not imaged
stained with synaptic proteins

Reference[BBC∗ 11]
[BBC∗ 11]immunolabeledimmunolabeled[BCK∗ 16]

1mm3

57K

133.7 M[SCJB∗ 21]

1.4 × .87 × .84mm3

120K

523 M

[CBB∗ 21]250 × 140 × 90 µm3

334

3.2 M

[DTM∗ 21,
SMBC∗ 20]1500 µm3
250 × 250 × 250 µm3

1897
25K

[KHB∗ 15]
[XJL∗ 20]

8 × 107 µm3

120variousvariousvariousvariousvariousvarious1700
20 M
only small subset
segmented
variousvariousvariousmultibeam SEM

2 ×30µm3

40K mitochondria

not segmented

Rat & Human
Zebrafish
&
Mouse

EM

2 × 30µm3 various

not segmented[WLFB∗ 20][WLL∗ 21]

EM & micro-CT

0.1mm3 0.25mm3

not segmented[LWP∗ 21]

Fruit Fly

TEM

0.023 mm3

18K axons170.000 nuclei &7.000 nuclei>1000 motor & sensory neurons

intuitively specify complex connectivity motifs and visualize themeffectively in both abstracted form and 3D. Visualization research
can help find interactive and scalable ways to reduce the complexity and dimensionality of connectivity motifs. Future research also
needs to investigate integrating more complex attributes into motif queries such as cell shape, brain region, and other topologicalinformation. Furthermore, how to query large sets of connectivity
subgraphs for meaningful motifs remains an open question.
In addition, more visualization research is needed to develop
methods for the interactive exploration and analysis of large neural wiring diagrams, supporting multi-scale connectivity analysis,
and the combined analysis of highly detailed spatial image data andlarge-scale connectivity data. Furthermore, with novel datatsets that
contain pre- and post-synaptic information, the visual explorationof functional connectivity is now becoming possible. Therefore, thevisual analysis of synaptic chains in the data based on morphological image features such as synapse strengths and the underlying
synaptic network is important for future research.

9.3. Multimodal Data Sources
Both optical imaging and EM come with specific drawbacks. EM
requires complex staining and can only image dead tissue, whilelight microscopy is limited in its resolution. Both imaging technologies complement each other, and it is beneficial to use thebest of both worlds. Functional data has been combined with
EM [CBB∗ 21], and also saturated reconstructions of living brain
tissue at nanoscale resolution has succeeded recently [VMM∗ 22].

[WCV∗ 14]

[ZLP∗ 18][Uni]
[BKK∗ 13]
[BBC∗ 11]

[PHG∗ 21]

These advances raise exciting questions, also for the data visualization community. How can we visually augment large brain circuitswith functional data? And how can we display changing connectivity patterns in living tissue over time? Combining multiple data
modalities will lead to new research in registration algorithms, e.g.,when combining the same tissue imaged with optical microscopes
and EM. When imaging living tissue in high resolution, neurons
and cell organelles move through the volume. Computer scientists
will need to develop new methods to identify interesting regionsof change and to track structures over time. Finally, how can weinterrogate these multimodal and multidimensional connectomics
datasets efficiently? Domain-specific query languages [BAAK∗ 13]
and techniques for intuitive visual user guidance are promising approaches and could allow neuroscientists to bridge the gap between
visual analytics and hypothesis generation.
10. Discussion and Conclusions
Connectomics data provide potential answers to many questions
about neural circuits that were not addressable previously. However, the challenge of approaching these questions is that connectomics data is not readily amenable to analysis, given its size andcomplexity. Until connectomics data began to be generated, neuroscientists would propose neuron structures and circuit architectures drawn as simple cartoon diagrams. Invariably, however, thesemodels are vastly oversimplified compared to actual neurites andsynapses in large-scale connectomics data. In this sense, large-scaleconnectomics data offers a paradigm shift by giving researchers, forthe first time, the opportunity to evaluate actual wiring diagrams© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

Table 5: Open source tools for connectomics. We categorize each tool’s focus as either A & R (Alignment & Registration), SEG (Segmentation), EXP (Exploration), or ANL (Analysis). We report on multi-user support (MU) and link to each tools’ website and source code.
Name
BigStitcher
TeraStitcher
MIST
TrakEM2
Vaa3D
FluoRender
Kimimaro
Ilastik
NeuTu
Paintera
WebKnossos
VikingViewer
CatMaid
VAST
PyTC
FlyWireDojo
Neuromorph
DotMotif
NeuPrint
Brainrender
Neuroglancer
NeuVidSharkViewer
NaVis
natverse
NeuroMorphoVis

A&R
•
•
•
•
•

SEG

•
•
•
•
•
•
•
•
•
•
•

Focus
PR

MU
EXP

•
•
•

•
•
•
•
•
•

•
•

•
•
•
•
•
•

•
•
•
•

•
•
•

•
•

•

•
•
•
•

•
•
•
•
•

Details

ANL

•
•
•
•
•

large scale image alignment + renderinglarge scale image stitching
stitching of large 2D image grids
image registration, annotation & segm.
visualization assisted analysis
confocal microscopy data analysis
automated skeleton extraction
object classification, counting & tracking
neuron reconstruction & visualizationneuron segm., reconstr. & proofreading
cloud-based 3D annotationterascale annotation
collaborative annotation
volume annotation & segmentation
deep learning-based neuron, synapse andorganelle segmentation
crowd sourced proofreading
semi-automatic collaborative proofreading
proximity analysis & center line extraction
neuronal motif search
connectivity exploration & analysis
data communication
large scale volume visualization
video generation
3D skeleton viewing
Neuron comparison & visualization
Neuroanatomical data analysis
Neuroanatomical data analysis

and discover the fundamental principles at play in neuron structures and circuit architectures. Visualization researchers now havethe rare opportunity to develop interactive frameworks that allow
neuroscientists to make sense of these data sets with tens of thousands of cells and hundreds of millions of synapses.
This report presented the state of the art of data visualization for
high-resolution connectomics data. We grouped related work along
the common connectomics pipeline. While most visualization approaches cluster towards the end of the pipeline, interesting visualization work has also been done in the early parts of the pipeline.
This work intends to summarize the field for both new visualization researchers and experts in the area. We also compiled a listof the latest and most commonly used publicly available data sets
and open-source tools in the field, highlighting the recent push foropen science. Connectomics research, in particular, relies heavily
on sharing data and expertise among research groups. This report
also reflects the interdisciplinarity of the field. Neuroscientists, machine learning experts, and visualization researchers need to collaborate closely. We argue that human-AI collaboration will play anincreasing role in translating the vast amounts of imaging data into
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

SourceCode
GithubGithubGithubGithubGithubGithubGithubGithubGithubGithubGithubGithubGithub
Executable

Reference

Github[LWLP21]GithubGithubGithubGithubGithubGithubGithubGithubGithubGithubGithubGithub[DMM∗ 22]
[HHM∗ 17]
[JBK18][MRJ∗ 21]
[CDU∗ 20]
[CTP∗ 21]
[MSBS∗ 21][Hub22][WBH14][SBJ∗ 21]
[BMJ∗ 20]
[AHE∗ 18]

[HRRP∗ 19][BI12][CMB∗ 17]
[CSS∗ 12]
[BIP15][WOH∗ 17]
[SBLW21][BKK∗ 19][ZOYP18][ZOYP18][BBB∗ 17]
[AMG∗ 11][SCHT09][BSL18]comprehensible knowledge. In turn, we hope that research findings
in connectomics will help incorporate more biologically-inspiredarchitectures into deep learning models.
We hope this review provides visualization and connectomics
researchers with a better overview of the different visualization
methods currently available for each step along the connectomics
pipeline. However, connectomics is a constantly evolving field withmany open visualization challenges. We hope to inspire future research in this exciting field with our survey. Tackling these challenges has the potential to bring us one step closer to the ultimate
goal of understanding the workings of the human brain.

Acknowledgements
We thank Z. Lin for his feedback. This research was supported by the National Science Foundation (NSF) Award Number IIS-1901030, NCS-FO-2124179, CNS1650499, OAC1919752,
ICER1940302, IIS2107224, an IBM-SUNY-IBM grant 2106 andthe King Abdullah University of Science & Technology (KAUST).

Beyer and Troidl et al. / A Survey of Visualization and Analysis in High-Resolution Connectomics

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