Abstractocyte: A Visual Tool for
Exploring Nanoscale Astroglial Cells
Haneen Mohammed, Ali K. Al-Awami, Johanna Beyer, Corrado Cali,
Pierre Magistretti, Hanspeter Pfister, and Markus Hadwiger

Fig. 1. Visual exploration of astrocytes. Our abstraction space panel (middle) allows users to intuitively navigate connectomics
datasets containing both neurons and astrocytes at different levels of abstraction. The two axes of the abstraction space correspond toastrocytes and neurites, respectively, and range from concrete to abstract. The curve depicts the path taken during exploration, which
can also be animated. (a) 3D mesh of astrocyte and neurites; (b) semi-transparent mesh and skeleton of neurites, simplified skeleton ofastrocyte; (c) abstract node-link diagram of neurites, heat map of astrocyte proximity; (d) 3D nodes for neurites, skeleton of astrocyte.
Abstract—This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale
volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study ofastrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires
high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and functionof astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With
Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously
analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing
astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point
allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between
different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate fromconcrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and theirrelationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We
describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system toassess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.
Index Terms—Connectomics, Neuroscience, Data Abstraction, Interactive 3D Visualization

1

I NTRODUCTION

In recent years, connectomics and the study of the detailed neuralconnectivity of the brain have become a main research focus in neuroscience. Scientists are looking at the mammalian brain at nanoscale
resolution to reconstruct individual nerve cell connectivity, and to shedlight on how the brain functions and develops. Interestingly, neurons(i.e., nerve cells) only make up about 50 percent of all cells in the mam-

• Haneen Mohammed, Ali K. Al-Awami, Corrado Cali, Pierre Magistretti, andMarkus Hadwiger are with King Abdullah University of Science andTechnology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
E-mail: {haneen.mohammed, ali.awami, corrado.cali, pierre.magistretti,
markus.hadwiger}@kaust.edu.sa.
• Johanna Beyer and Hanspeter Pfister are with the John A. Paulson School ofEngineering and Applied Sciences at Harvard University, Cambridge, MA,
USA. E-mail: {jbeyer, pfister}@seas.harvard.edu.
Manuscript received 31 Mar. 2017; accepted xx xxx. 201x. Date of Publication
xx xxx. 201x; date of current version xx xxx. 201x. For information on
obtaining reprints of this article, please send e-mail to: reprints@ieee.org.
Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx

malian brain [4]. The other half is made up by glial cells. These cells
are typically located in the vicinity of neurons and provide supporting
features for them. For example, they supply nutrients and oxygen,provide insulation, and form myelin. Astrocytes are a subtype of glial
cells, which are crucial for regulating the transmission of electricalimpulses within the brain, and also act as glycogen stores to supply
neurons with energy. However, even though astrocytes are essential
for the brain to function, they have so far received relatively little
attention in connectomics research. One reason for this is probablythat astrocytes are difficult to study. For example, the morphology ofastrocytes is even more complex than that of neurons. Since they aresupporting structures in the brain, they inherently connect to everything,making a detailed connectivity analysis complicated, if not impossible.
Furthermore, while neurons have well-defined subparts (e.g., axons,dendrites, spines) that can be analyzed individually, astrocytes are muchless structured, making a detailed analysis of individual parts difficult.
Most visualization and analysis tools for connectomics have focusedon the synaptic connectivity of neurons, or on the overall structure
and morphology of neurons in the brain [1, 2, 7, 9, 34]. To our knowledge, no visualization tool has tackled the analysis of astrocytes so far.
When looking at astrocytes, neuroscientists are not only interested inthe astrocytes themselves, but also in how they interact with nearby
neurons. Therefore, analysis tools should be able to highlight or indicate connections or closeness of astrocytes to neurons at different
scales and levels of detail. For example, showing the entire astrocyte
in a high-resolution 3D rendering will lead to clutter and obstructed
views due to the highly complex morphology. However, a detailed
3D rendering might be the best choice when focusing on small details.
Furthermore, while neurons also have a branching morphology at the
microscopic level, at nanoscale resolution neurons are usually a lot lesstangled than astrocytes. Therefore, during the analysis, it is crucial tobe able to dynamically specify the level of detail that should be used in
the current visualization of astrocytes and neurons, respectively.
In this paper, we present Abstractocyte, a novel tool for the visual
exploration of astrocytes and neurons. We tackle the inherent data complexity of nanoscale brain tissue by supporting a multitude of different
visualizations that display the data at different levels of abstraction.
Visualizations range from detailed 3D surface renderings of segmented
structures to simplified abstractions, e.g., skeletons and graphs. We
allow the abstraction of both entities (i.e., astrocytes and neurons) independently of each other. This is necessary because biologists need
to be able to explore both types of cells in combination, but often
at different levels of detail, e.g., a detailed mesh of a neurite with asimplified skeleton of the astrocyte. We achieve this by presenting anovel interaction interface that we refer to as an abstraction space (seeFig. 1). This conceptual space allows users to intuitively explore thespace of possible abstractions. Furthermore, we support the analysis of
the glycogen distribution in astrocytes. Our collaborators are currently
investigating whether the glycogen distribution within an astrocyte iscorrelated with factors such as the synapse strength of nearby neurons.
Therefore, we cluster and display the glycogen distribution, and map it
to structures of interest, e.g., to nearby synapses or mitochondria.
The main contribution of our work is the design and implementation of Abstractocyte, as a novel tool for the interactive visualizationof nanoscale astroglial cell morphology. We introduce an abstraction
metaphor that allows users to explore astrocytes in relation to theirsurrounding neurons at different levels of visual abstraction, for astrocytes and neurons, respectively and independently. Abstractocyte also
supports the visual analysis of the detailed glycogen distribution andneurite proximity of astrocytes. Furthermore, we demonstrate an intuitive and flexible user interface for exploring the 2D visual abstraction
space, which enables interactive and consistent transitioning between
2D and 3D visualizations. We illustrate the usefulness of Abstractocyte
in practice via three case studies from our collaborators in neuroscience.
2

R ELATED W ORK

Neuroscience and Connectomics. Connectomics aims to reconstruct
the detailed connectivity diagram of neurons in the brain using highresolution and high-throughput imaging [27]. The main goal is tounderstand the relation between structure and function of the brain [31].
Visualization for Connectomics. Pfister et al. [30] give a survey ofdifferent techniques for macro-, meso-, and microscale connectivity
visualization for connectomics. An overview of different frameworks
for visualizing the human connectome is given by Margulies et al. [28].
Most previous visualization tools for connectomics focus on highlevel connectivity visualization between entire brain regions. In theseframeworks actual cell morphology is typically abstracted, and thefocus lies on depicting the connectivity between regions or a networkof neurons. For example, NeuroMap [34] uses circuit wiring diagrams
to represent possible connections between neurons. Other tools forhigh-level connectivity visualization make use of 2D projections for 3D
tractography data [21] , or use matrix visualizations for showing connectivity information [29]. NodeTrix [38] is a system for the block-wise(i.e., region of interest based) comparison of human brain networks.
Several toolkits have been developed for the visual analysis of brain
networks [17, 26]. All of these methods, however, focus on high-levelor macro-scale connectivity information between entire brain regions.
Our collaborators are interested in nanoscale brain morphology andconnectivity at the level of individual synapses. On a cell level, individual neurons can be represented as nodes in a connectivity graph,with synapses being the actual links between nodes. Examples for thisare shown in the Viking Viewer [3] and ConnectomeExplorer [7]. Several tools allow users to define interactive queries for analyzing brain
connectivity data, such as BrainGazer [9] or ConnectomeExplorer [7].
None of these tools, however, support the visualization of astrocytes.
Astrocyte analysis and visualization. Braumann et al. [8] have introduced an image processing chain to generate structural descriptions ofastrocytes based on graphs. They use confocal laser scanning data andtherefore work on the macro level. Suwannatat et al. [36] use confocal
microscopy and probabilistic segmentation to visualize astrocytes of theretina. They use a glyph based visualization where individual glyphs
are scaled according to the uncertainty of the segmentation. More recently, Calı̀ et al. [10] have used a CAVE for visualizing high-resolution
neuronal EM datasets. They also segment and render glycogen granules
using Blender and NeuroMorph [22]. None of these methods, however,support an interactive exploration of the data in 3D with dynamicallychanging levels of detail or abstraction.
Visual abstractions for biological structures. To analyze the connectivity of individual neurons, Neurolines [2] introduces a visual
abstraction of neurites that reduces clutter and converts the 3D structureof neurons into a 2D visualization. A different 2D abstraction of 3D
data was introduced by Borkin et al. [7] for the visualization of artery
trees for the diagnosis of heart disease. More generally, CPR (curved
planar reformation) [23] allows 3D tubular structures such as vessels to
be displayed in a 2D visualization with minimal loss of information. All
these methods focus on anatomical structures that can be represented
as trees or a collection of trees. However, the nanoscale morphologicalstructure of astrocytes is more difficult, and typically contains loops.
More recently, Smit et al. [32] used a topological similarity measure
and an abstract graph representation to visualize anatomical variations
in complex branching structures for educational purposes. Sorger et
al. [35] used visual abstractions for molecular visualization. They propose abstraction transforms for smooth transitions between different
visual models (e.g., different developmental phases of a virus).
2D selection/input panels. Kniss et al. [23] introduced multidimensional transfer functions for GPU volume rendering. They also proposed a 2D classification widget or panel that allows users to interactively specify the transfer function mapping in 2D. A more general
concept of interactive UI sliders was recently presented by Tsandilas
et al. [37]. They propose a sketching interface for exploring multidimensional datasets. Abstractocyte uses a panel that allows users tointeractively explore the abstraction space of possible visualizations.
Users can freely navigate within the 2D panel, and directly see theircurrent position in the abstraction space visualized in the main view.
Animated transitions between visual representations. Heer andRobertson [19] have shown that animated transitions between different
statistical charts improves graphical perception. Elmqvist et al. [13] introduce animated transitions for exploring multidimensional data with
scatterplots, using animated rotations in 3D space. More recently, Guilmaine et al. [18] have presented four techniques for animating changes
in radial tree visualizations. Regarding volumetric data, Basch [5]has investigated animated transitions across multiple dimensions, forexample, transitions between volume rendering and histogram views.
Zwan et al. [39] propose a technique for navigating between nested abstraction levels in volumes, using image-based halos and magic lenses.
They demonstrate their technique on a fluid flow visualization. In thispaper, we focus on smooth transitions between different abstractionlevels of volumetric biomedical data. The user can seamlessly switchfrom highly detailed surface rendering to simplified views such as 3D
skeletons or 2D node-link diagrams.
Network layouts. Connectomics focuses on the connectivity of neurons. Therefore, most visualization tools for connectomics include
network and graph visualization components. Node-link diagrams are
the most common form for representing the connectivity between brain
regions and/or individual neurons. If the inherent 3D structure of the
anatomical features is still available, connectivity can be shown on topof the original spatial position [9, 29]. In more abstract connectivity
visualizations, on the other hand, the spatial location of nodes is completely independent of their original spatial positions [7, 34]. Hybrid

Dendrites

Cell
body

NeuronGlia

Axon

BoutonAxon
SynapseSpine
Dendrite

Astrocyte

Fig. 2. Structure of neurons and glia. Neurons transmit signals fromaxons to dendrites via synapses. Glia are cells that provide supporting
functions for the neural system. They are highly interconnected withneurons, but do not transmit nerve impulses. Astrocytes are a type of
glial cell that are especially important for synapse formation and function.

approaches like Neurolines [2] retain some of the spatial informationin their 2D graph layout. In Abstractocyte we use force-directed graphlayouts to compute the abstract 2D views of our data. Kobourov [24]gives a good introduction to force-directed graph drawing algorithms.
Fruchterman and Reingold [16] originally proposed spring forces where
there are repulsive forces between all nodes, but also attractive forces
between nodes that are adjacent. Later, Frick et al. [15] added new
heuristics to the original spring forces approach by Fruchterman andReingold. In Abstractocyte we have added additional heuristics to ourgraph layouting algorithm that are specific to our domain (Sec. 6.1).
3 B IOLOGICAL BACKGROUND
The average human brain contains around 100 billion neurons. Neurons
are highly interconnected, and transmit nerve impulses by forming
synapses with each other. A single neuron typically consists of a cellbody, multiple dendrites, and a single axon. See Fig. 2. Dendrites aretree-like structures that receive signals from other neurons, while axons
are long tubular structures that extend from the cell body to forward
signals to other neurons. The term neurite can be used to describe
either an axon or a dendrite. Synapses are made up by a bouton on theside of the axon, the synaptic cleft, and the post-synaptic terminal on
the receiving dendrite. Dendrites can have synapses either directly on
their trunk, or on small protrusions called dendritic spines.
While neurons make up around 50 percent of the cells in the mammalian brain, the other half is made up by glial cells [4]. Glial cells are
non-neuronal cells that typically surround and insulate nerve cells, andprovide supporting features for neurons [20]. An astrocyte is a type ofglia that is star-shaped (Fig. 2). It is mainly responsible for providing
nutrients, balancing the amount of neural transmitters, and maintaining
the blood-brain barrier. Astrocytes make up 20 to 40 percent of all glia,
and are important for synapse formation and function [12]. They also
act as glycogen stores to supply neurons with energy. Our collaborators
are interested in the distribution of glycogen granules within astrocytes,
and whether there is a correlation between glycogen granules and biological features such as synapse strength. They also want to investigate
how the mitochondria distribution in astrocytes influences cell behavior.
3.1 Neuroscience Workflow
Our collaborators start their workflow by scanning a mouse brain using
electron microscopy, before they register the individual scanned slices
to form a consistent 3D volume. The size of an imaged tissue block
is typically five microns in each dimension, which is imaged at aresolution of 5 × 5 × 15 nm per voxel. This results in volumes of about
1024 × 1024 × 450 voxels in size. These volumes are then typicallysegmented using the semi-automatic methods provided by Ilastik [33]and TrakEM2 [11]. Segmentations are created for all astrocytes, axons,dendrites, synapses, spines, boutons, glycogen, and mitochondria.

4

A BSTRACTOCYTE D ESIGN

The idea of Abstractocyte originated in meetings with our collaborators,
where they described their problems in analyzing and exploring dataacquired for investigating astrocytes in an intuitive manner. Biological structures in brain tissue at nanometer resolution tend to have an
incredibly complex morphology that easily leads to clutter or occlusion
in 3D visualizations. However, not all analysis and exploration tasks
require the data to be shown with full detail. It can often be betterto show different structures at different levels of detail or abstraction.
Therefore, the main goal of our work was to provide neuroscientists
with the framework necessary to study brain cells, including nerve cells
as well as glial cells, in an interactive and intuitive manner, at the visual
abstraction level that is best suited for each individual task. To achievethis, users have to be able to seamlessly navigate between different visual abstraction levels, to reduce complexity while preserving essential
features related to morphology, connectivity, and proximity.
4.1

Domain Goals and Tasks

The design of Abstractocyte supports the high-level domain goalsof neuroscientists to (a) explore the detailed morphology of astrocytes; (b) analyze proximity patterns between astrocytes and neurites (also called astrocytic coverage); and (c) analyze energy (orglycogen) distribution patterns. These overall goals can be broken
down into the following specific domain-oriented tasks:
T1 – Explore the astrocytic coverage of neurites and synapses. Our
collaborators are interested in how much of a structure (e.g., a synapse)
is covered or touched by an astrocyte. Synaptic coverage is plastic—it
is different depending on the brain area and the activity of the synapses.
Our collaborators want to investigate whether there is a causal relationship between coverage, synaptic plasticity, and synapse maturation.
T2 – Synapse glycogen analysis. Currently, there is still a debate inneuroscience whether boutons or spines are energetically more expensive during synaptic transmission. Therefore, our collaborators want
to investigate how and to which extent glycogen granules (which area precursor of lactate, and therefore an energy store located in astrocytes) are related to boutons and spines. Looking at synapses and theirproximity to glycogen clusters will be a first step in that direction.
T3 – Mitochondria glycogen analysis. Our domain experts are investigating whether the presence of mitochondria in neurons can help
in the interpretation of the spatial relationship of glycogen granules
and synaptic elements. For example, dendrites that have mitochondria
along their shaft might accumulate glycogen granules around excitatory
shaft synapses, from which spines generate. This would suggest an
involvement of astrocytic energy in the formation of dendritic spines.
T4 – Astrocytic mitochondria coverage of synapses. Mitochondria
are the powerhouses of the cell. In astrocytes, mitochondria can haveother functions, such as modulating intracellular calcium signaling.
This has been shown to be related to synaptic activity, and can be modulated by mitochondria. Therefore, our collaborators want to explore
and compare the locations of astrocytic mitochondria and synapses.
T5 – Astrocytic mitochondria coverage of their parent astrocyte.
Mitochondria locations within astrocytes are not spread out, but appear
preferentially in microdomains. Our collaborators want to explore
whether this is related to the presence of synapses and, therefore, want
to study the intracellular distribution of mitochondria within astrocytes.
5

A BSTRACTION S PACE

The necessity of supporting different levels of visual abstraction comesfrom the observation that the optimal visual representation of neurites
and astrocytes changes depending on the current user objective. For example, when neuroscientists want to explore the neuronal connectivity,
they prefer to use an abstract node-link diagram for neurites rather thana highly detailed mesh (Fig. 1c). On the other hand, when they want toanalyze how close neurites are to a specific astrocyte, they would want
to look at the neurite in more detail, and either color-code the proximityof the astrocyte onto the neurite’s surface, or show the astrocyte as amorphological 3D skeleton (Fig. 1b). In order to be able to do this,

2D
views

abstract

one specificabstraction level

abstract

Interpolation:

2D
views

face
interpolation
edge
interpolationnointerpolation

3D
views

concrete

2D
3D

a)

abstract

3D
views

concreteb)

abstract

Fig. 3. Visual abstraction space. Left: We define three interpolation methods to support smoothly navigating the 2D abstraction space: faceinterpolation, edge interpolation, and no interpolation. When choosing visual abstractions, our system restricts user input depending on the supported
modes of interpolation between specific abstraction levels. Right: (a) Our abstraction space supports transitions between 2D and 3D views, which
are conceptually arranged on separate layers. (b) In the user interface, we depict these layers in simplified form, using a diagonal edge between
them. Edge interpolation for the transition from 3D to 2D, or vice versa, enforces a simultaneous change of the abstraction level for both neurites andastrocytes. All other abstraction levels in Abstractocyte currently use face interpolation, where both abstraction levels can change independently.

scientists need to be able to change the level of abstraction for different
structures on the fly, as their analysis progresses.
In Abstractocyte, we introduce a 2D visual abstraction space thatallows us to abstract and simplify the visual representation of structures
along two orthogonal, conceptual “axes” (see Fig. 1, center). The firstaxis represents astrocytes, and allows users to seamlessly switch therendering of astrocytes from detailed (e.g., high-resolution 3D meshes)
to abstract (e.g., a 2D node-link diagram of the astrocyte’s morphology).
The second axis represents neurites, and allows interactively changing
the visual representation of neurites. Fig. 1 shows this abstraction space
and the visual abstractions at four selected points within that space.
5.1

Abstraction Space Elements

The abstraction space is spanned by the two axes that represent neurites and astrocytes, respectively, going from concrete, at the origin,to abstract. Supported specific abstraction levels along each axis are
represented by points or nodes along that axis. Combined, the abstraction levels of both axes create a 2D grid of specific abstraction levels.
Fig. 3 (left) shows the different elements in the abstraction space:
Nodes: Each node represents a specific combination of a neurite andan astrocyte abstraction level (e.g., a 3D mesh for one structure, and a
3D skeleton for the other one). At the exact position of a node, no interpolation between abstraction levels is necessary. The representations
for neurites and astrocytes are combined directly during rendering.
Edges: We support edges between two nodes that are either axisaligned, or at a 45-degree angle. In the former case, the edge represents the linear interpolation between two abstraction levels along thecorresponding axis. In the latter case, the edge represents the linear
interpolation between abstraction levels for both axes at the same time.
A 45-degree edge ensures that the abstraction levels of both axes arechanged simultaneously. This is necessary in certain cases to ensure
consistency of the visualization, for example, when moving from a 3D
representation to a 2D representation. Usually, a visualization where
one of the axes has a 2D, and the other one a 3D representation is notmeaningful. Therefore, we prevent this by restricting user input to a
45-degree edge, so that the dimensionalities for both axes are linked.
Faces: Four neighboring nodes in the abstraction space span an area.
Selecting a point inside that area will automatically compute an interpolated abstraction level. We compute the interpolated value along eachaxis separately, and use these values to create the visual abstractions
for neurites and astrocytes, respectively. The combination of neurite
and astrocyte abstraction levels is done automatically during rendering.
Interpolation: For every node and combination of nodes we specify
what type of interpolation it supports depending on the underlying
abstraction. By default, we allow full face-based interpolation between
nodes. However, we can restrict user navigation inside the abstraction space to edges, i.e., the abstraction level can only be modified
along edges, or even disallow interpolation altogether. The different
interpolation options that we support are illustrated in Fig. 3 (left).

Fig. 4. Abstraction space GUI panel. (Left) Users can draw and savepaths in the abstraction space for a continuous animation through the abstraction levels along them. (1, 2, 3) Paths can be replayed or navigated
via a slider to revisit specific levels of abstraction. (1) Detailed surface
rendering of a spine (orange) connected to four different boutons (teal),
and close to mitochondria (blue); astrocyte proximity is visualized using
splatting and color coding (red) on the neurite surfaces; (2) Neurites as
skeleton; (3) 2D node-link diagram showing the connectivity of neurites.

Switching dimensionality of the visual abstraction: Conceptually,
the abstraction space allows switching between arbitrary visual abstraction levels of the underlying data. However, when switching thedimensionality of the underlying data representation and visualization,some extra considerations need to be taken into account. First, wewant to visually display this switch in dimensionality in the abstraction
space. For this, we have extended the abstraction space to the conceptof layers (Fig. 3a). In the top right corner, the abstraction switchesfrom a 3D representation to a 2D representation. Therefore, we indicate
the transition from a 3D to a 2D representation by moving from theupper layer (3D) to the layer below (2D). This also allows us to restrict
user navigation to edge interpolation in the area where the abstraction
moves from one dimensionality to another. Using edge interpolation
makes sure that the resulting visualization is always consistent (i.e.,
valid) and that it does not mix arbitrary 2D and 3D abstraction levels.
After feedback from our collaborators, we have further adjusted theuser interface to the simplified layer representation shown in Fig. 3b.
5.2 Abstraction Space GUI PanelOur 2D user interface panel for navigation inside the abstraction space
is shown in Fig. 4. Users can freely move the mouse pointer in order to select or change abstraction levels for neurites and astrocytes,
respectively. As the user moves between different locations in thepanel, we smoothly interpolate between abstractions, depending on thecurrent position and its neighborhood. However, at the same time oursystem restricts user interaction to those areas in the panel where valid
abstractions have been defined (invalid areas are grayed out).
We also allow users to draw entire paths in the GUI panel (Fig. 4,left), which allows re-using and replaying a sequence of abstractions.
This is very useful for repeating standard analysis tasks, where users
can pre-define which levels of abstraction they need at what point, as
well as for presentation and teaching purposes, to reproduce an analysis.

Fig. 5. Astrocyte abstraction levels. Astrocytes displayed at different levels of abstraction. (a) Highly detailed 3D mesh (L0); (b) Transitional phase,where the 3D mesh contracts and fades to a skeleton (L1.5); (c) Simplified skeleton together with clustered glycogen granules (L3); (d) Astrocyte
shown as proximity map on top of neurites (L4, 3D); (e) Astrocytic coverage shown in 2D node-link diagram of neurites as a heatmap (L4, 2D).

6

V ISUAL A BSTRACTIONS FOR B RAIN C ELLS

The main goal for visualizing neurites is to be able to identify importantregions and sub-structures, such as the sites of synapses (i.e., boutons
and spines), and their relation to astrocytes. The main goal for visualizing astrocytes is to allow scientists to explore their proximity toneurites, while at the same time showing important information about
the astrocyte (e.g., glycogen granules or mitochondria). In collaboration with our domain scientists, and keeping in mind the overall domain
goals, we have identified the following visual abstractions (Fig. 5) :Abstraction Level 0 (L0):
We use surface rendering of the high-resolution 3D
mesh of the segmented data (i.e., neurites, astrocytes,
and all their segmented sub-structures). This is the mostdetailed visualization, and allows users to see the data
in its original form and highest detail. Structures arecolor-coded by type, to quickly provide an overview ofwhat type of structures are present.
Abstraction Level 1 (L1):
We use surface rendering with non-photorealistic
shading. We have chosen toon shading to retain the overall 3D impression, while abstracting minor bumps oredges from neurites or astrocytes, respectively. This isuseful when looking at astrocytes, as the high-resolution
mesh is often too detailed and distracts from overallmorphological patterns.
Abstraction Level 2 (L2):
We render the 3D skeletons of neurites or astrocytes,
respectively. Skeletons reduce clutter in the visualization, while still maintaining important spatial cues. The
high-resolution mesh of an astrocyte/neurite often occludes synapse details, while a skeleton is a sparser visual encoding that still allows seeing morphological (ortopological) features of the structure. By pre-computing
a mapping of each mesh to its skeleton, we can smoothlyinterpolate between the two representations.
Abstraction Level 3 (L3):
We present neurites/astrocytes as simplified 3D skeletons, where branches have been straightened. This
abstraction can be used to highlight 3D connectivity.
Anatomical details are removed, while the user still gets
an impression of the location, orientation, and branching pattern of a neurite. Our collaborators use this mode
when they focus on branching patterns and properties.
Abstraction Level 4 (L4):
In this abstraction level, we use different representations for neurites and astrocytes, respectively. We
further distinguish between abstraction level 4 in 2D
or 3D views, and adjust the representation accordingly.
Neurites are displayed as nodes. In the 2D view, weshow a full node-link diagram where neurites are represented as nodes, while connections between them (i.e.,
synapses) are shown as links between nodes. In the 3D
view, neurites are also shown as nodes. However, weomit the links between them. The reason for this is that we typicallyhave a high number of links, and 3D node-link diagrams get clutteredeasily. This is the most abstract neurite visualization, and is mainly
used for exploring synapses and connectivity. Optionally, we can map
the volume of a neurite to its node size, to give an indication of its size.
The visual encoding of astrocytes in L4 is done differently. We do
not render astrocytes as their own primitives anymore, but project them
onto the closest neurite’s surface. This allows us to show the distribution
or coverage of astrocytes on neurons and their substructures. This isespecially useful to judge how close an astrocyte is to a given neurite.
Combining different visual abstractions: The power of Abstractocyte comes from the ability to combine different visual abstractionlevels for neurites and astrocytes. This allows users to pick the most abstract level for their task that still provides them with enough details toanswer their current questions. Furthermore, we can display additional
information on a segment’s surface mesh, skeleton, or connectivity
graph, by using color mapping and density fields (Sec. 7.2).
6.1

Transitions between Abstraction Levels

While users navigate the abstraction space (by moving the current
mouse position), all views are continuously updated to reflect the corresponding levels of abstraction. Positions that are between specificlevels of abstraction are interpolated using the interpolation methods
described above. For transitioning between two abstraction levels thatboth employ a 3D visualization (i.e., meshes, skeletons, simplified
skeletons), we linearly interpolate between the two 3D abstractions. In
particular, we interpolate shading for transitioning to non-photorealistic
rendering, and we interpolate vertex positions and opacity for transitioning between a mesh, a skeleton, or a simplified skeleton.
3D to 2D transitions. Transitioning from a 3D representation to a
2D representation is handled differently. First, we restrict user input toedge interpolation, so that astrocytes and neurites are switching dimensionality at the same rate. Next, we use a customized, force-directedlayout algorithm to convert 3D graphs into valid 2D representations [24].
Neighboring nodes in a force-directed layout repel each other unlessthey are connected, in which case they attract each other. We haveadded three new rules to the algorithm to make sure we achieve an
optimized layout. First, nodes are attracted to the center of the canvas.
This ensures that the graph does not expand rapidly. Second, nodes are
attracted to their original 2D screen position, i.e., their position before
starting the force-directed layout. Third, nodes are constrained to staywithin a certain radius of their original 2D screen position. Together
with the previous rule, this keeps the graph similar to the original 3D
skeleton, retaining its morphological form while resolving occlusions,and facilitating smooth transitions.
Additionally, when transitioning from a 3D to a 2D view, we adjust
the weights described above for the force-directed graph algorithm
according to the current interpolation position, to achieve the desired
3D to 2D graph layout transition. The individual forces are accumulated
and applied to the nodes to update their positions accordingly. Thisis repeated until the graph is in a steady state, or the user changes theabstraction level or the viewport.
To optimize the performance of the force-directed graph layouting,
we use 2D spatial hashing to find neighboring nodes efficiently.

Fig. 7. Visual proximity analysis. (a) The astrocyte (red) occludes theneurites (cyan and yellow), making it hard to see where the astrocyte
touches or is in close proximity to the neurites; (b) The proximity of
the astrocyte is visualized on the neurite’s surface using splatting andcolor mapping (the red areas on the neurite surface). The proximity ofastrocytic mitochondria is visualized in the same way (purple areas).

Fig. 6. Abstractocyte user interface. The filtering and analysis panel
is displayed on the left. It allows users to interactively filter, select, andsort the data. The abstraction space panel is shown at the bottom right.

7

V ISUAL A NALYSIS

The main goal of the different visual abstractions introduced above isto support scientists in the analysis of their data. In accordance with
our three main domain goals described in Sec. 4.1, we support the threedifferent exploration and analysis scenarios described below.
7.1

Visual Exploration of Morphology and Topology

After acquisition and segmentation of a new dataset, the first objectiveof our collaborators is to get a good mental image of the data. Therefore,
exploring the morphology and topology of astrocytes as well as neurites
is typically their first objective. Allowing users to look at their data
at different abstraction levels, with smooth transitions between levels,
helps them to identify morphological features for cells of interest. For
example, when using a skeleton representation for an astrocyte it iseasier to follow branches, and see how they interact with their surroundings. On the other hand, a simplified 2D graph representation allows
users to quickly see the connectivity between structures, without having
to manually trace and follow structures in 3D. In general, there is noone specific abstraction level that allows users to see all interesting features of a cell of interest. Only frequent transitioning between different
levels of abstraction enables scientists to gain a detailed understandingof their data. Being able to freely navigate inside the abstraction spaceis particularly important for tasks T1 and T5 (Sec. 4.1), to explore theoverall relations of neurites, astrocytes, and mitochondria.
In addition to providing visual abstractions, we allow users to filter
their data on-the-fly, to exclude unimportant structures from analysis
and visualization. Data can be filtered by manual selection directlyin the visualization, or by using a user interface filter panel. Thispanel supports a combination of filters, based on object type, object ID,
and additional parameters, such as connectivity, parents, and substructures (Fig. 6). Filtered-out objects are removed from all subsequentvisualization and analysis steps. This is important for tasks T1 to T5.
7.2

Visual Proximity Analysis

One of the most important tasks when analyzing brain cells and networks is spatial proximity analysis. In Abstractocyte, we can visualize
the proximity of a cell structure A to another structure B, by conceptually “splatting” structure A onto the visualization of structure B. See
Fig. 7b for an example. To be able to do this interactively, we firstcreate a 3D binary segmentation texture for structure A, setting voxels
belonging to A to one, all other voxels to zero. If available, we create
this texture directly from voxel segmentation data. If not, we voxelize
the 3D segmentation mesh of structure A. In the next step, we use thistexture for distance queries while rendering structure B. Specifically, atevery vertex position of B, we take a number of samples in the vicinityof that vertex in the previously generated 3D texture of structure A to

Fig. 8. Glycogen analysis and visualization. (a) Clustered glycogengranules depicted in combination with the astrocyte’s skeleton; (b) Glycogen density mapped onto the toon-shaded 3D mesh of an astrocyte.

estimate the distance between the two structures. We usually weight
the resulting distance with a Gaussian curve to obtain a smooth falloff. Using this basic approach, we visualize spatial proximity in two
different ways, depending on the dimensionality of the visualization:
Proximity in 3D. In the case where structure B is visualized as a 3D
mesh or a 3D skeleton, we color-code the surface of the mesh or theskeleton using the technique above. When color-mapping values onto askeleton’s surface, we can either directly use the skeleton coordinates
to query the distance values, or we use the corresponding mesh vertex
coordinates. Fig. 5d and Fig. 7b depict examples of this approach.
Proximity in 2D. For more abstract 2D representations, e.g., a nodelink diagram of neurites, we can visualize the distance values as a heatmap on top of the network diagram. For this, we further splat all neurite
nodes into a 2D texture, and attenuate a node’s intensity by its distanceto the astrocyte. The resulting 2D texture can directly be displayed as aheat map on top of the graph. This approach is depicted in Fig. 5e.
Filtering. Abstractocyte also allows on-the-fly filtering based on
proximity and coverage. This allows us to restrict the visualization to
structures within a certain minimum and/or maximum distance to aselected structure. For example, this allows looking only at synapses
with a minimum amount of astrocytic coverage. Visual proximity
analysis supports tasks T1 (to analyze astrocytic coverage of neurites),
T2 and T3 (to analyze glycogen), and T4 (to analyze mitchondria).
7.3

Visual Analysis of Glycogen Granules

A major objective of Abstractocyte is to allow scientists to analyze theglycogen distribution in astrocytes, and to explore the relationship between glycogen cluster locations and synapses. Glycogen granules are
stores of energy in the form of glucose. Brain energy consumption is ofmajor interest in neuroscience, where the goal is to find the mechanismsof how energy is being regulated, distributed, and absorbed by neuronalcells. Astrocytes hold and regulate a lot of that energy in the formof glycogen. To tackle these questions, our collaborators identify andmark the spatial locations of biological granules (in our case glycogengranules) in the microscopy data. Glycogen tends to appear in clusters in different locations inside the astrocyte. We have implemented
on-the-fly clustering of granules using the DBSCAN (density-based
spatial clustering of applications with noise) algorithm [14].
We support two different ways of glycogen visualization: First, we
can visualize individual granules directly by rendering them as small
spheres in 3D (Fig. 8a). We can also color-code granules based on theircluster membership. Second, we can compute granule density maps that
we can then use in our 3D and 2D visualizations (Fig. 8b). Computing
the granule density map is done similarly to computing the proximity
between structures (Sec. 7.2), and allows us to map granules to theirclosest subcellular structures. Optionally, we can also use granuleclusters instead of individual granules for computing and displaying acluster density map on the surface of the closest biological structures(Fig. 8). For quantitative analysis, users can interactively explore
clusters and their associated attributes, e.g., the number of granules, or
total volume. The analysis of glycogen is important for tasks T2 andT3, to analyze glycogen in relation to synapses or mitochondria.
7.4

Implementation

Abstractocyte is implemented in C++ and OpenGL 4.3, using Qt for
GUI elements. The application runs on a standard Windows PC andrequires a recent GPU for rendering. All case studies were performed
on a Windows 10 PC with a NVIDIA GeForce GTX 1070 GPU.
Typical mesh sizes in Abstractocyte are around 8 million vertices
with 16 million faces, which can be rendered interactively at more than
40 fps. Our splatting technique for showing proximity values on an
object’s surface is also performed dynamically at run time, and achieves
framerates of around 10 fps. Clustering of glycogen can be done atrun time, and typically takes less than a second for 4,000 granules.
Computing the force-directed layout updates for switching from 3D to
2D views is done iteratively whenever users move the selected position
along a 3D-to-2D edge in the abstraction space and can be done inreal-time as well. We run the force-directed layout algorithm as a
background thread to make sure that users can still interact with oursystem while the layout for the 2D views is being computed.
8

C ASE S TUDIESTo demonstrate the utility and flexibility of our framework, we present
three case studies that we have recorded in user sessions with ourcollaborating domain scientists. All three cases were performed bya neuroscientist who is working in the field of connectomics and has
several years of experience with analyzing electron microscopy data ofbrain tissue. Prior to the case studies, the neuroscientist had a one hourhands-on training session to get familiar with Abstractocyte.
8.1

Fig. 9. Case study 1: Evaluating the astrocytic coverage of spinesand boutons. (a) Node-link diagram showing neurites as nodes andsynapses as links (yellow: dendrites, orange: spines, blue: boutons,
cyan: axons). The astrocytic coverage of neurites is shown in the nodelink diagram as a heat map; (b) The user selects a node (spine) with highcoverage, and filters the graph according to the node’s connectivity; (c)3D rendering of the spine (orange) that seems to make a synapse with anaxon (faded cyan). This synapse was missed in the initial segmentationof our collaborators and only discovered by using Abstractocyte.

Data

For our evaluation and during the initial testing of Abstractocyte ourcollaborators have analyzed six different electron microscopy volumes.
All datasets are from mouse brains, specifically from layer one of thesomatosensory cortex. Three volumes are from young adult mice (i.e.,around 90 days old), while the other three volumes are from old adultmice (i.e., 3 years old). Each dataset consists of a single astrocyte androughly 1,000 segmented objects (excluding glycogen granules). For
our case studies, our collaborators have used a dataset containing 355
axons and dendrites, 388 spines and boutons, 167 synapses, and 181
mitochondria. In addition to these segmented objects, more than 4,000
glycogen granules have been labeled manually using TrakEM [11].
To prepare the data for our visualization tool, we extract high-quality
meshes and skeletons of segmented neurites and astrocytes in a preprocessing step. For extracting meshes, we use NeuroMorph [22],and for computing the skeletons we use a GPU-based framework [25]that implements symmetric parallel and persistence thinning [6]. In
addition, we compute the centroid of each object and pre-computecorrespondences between each 3D mesh and its skeleton. We computeand store the closest point on the skeleton for each vertex in the mesh,so that we can interactively transition and interpolate between the two.

8.2 Case Study 1: Astrocytic Coverage of Synapses
Astrocytic coverage refers to how many neuronal structures and substructures are in contact (or lack of contact) with astrocytes. Our
collaborators are interested in how many structures are in contact withastrocytes, but also what the percentage of coverage/proximity is for aselected structure, such as a synapse. Astrocytes play an important rolein synaptic physiology. For instance, in the recycling of neurotransmitters. This function is crucial for synaptic transmission performance, as
excess of neurotransmitter in the synapse might lead to misactivationof neighboring synapses, synaptic over-activation, and eventually cell
death. Astrocytic wrapping of the synapse limits these effects, which is
why our collaborators wanted to investigate this in their datasets.
The scientist started the session by first getting a high-level overview
of the neurites in the dataset using the node-link diagram (Neurite
L4, 2D). Next, he looked at the astrocytic coverage of neurites in
the node-link diagram, by enabling the heat-map visualization for
astrocytes (Astrocyte L4, 2D). He quickly found a spine node withhigh astrocytic coverage (radiating red node in Fig. 9a). Abstractocyte
can quantitatively estimate the total coverage of a structure (in thiscase the spine) by computing the average distance of all vertices in thespine to the astrocyte. The smaller this value, the closer a structure
is to the astrocyte. Next, the scientist examined the spine and filtered
the dataset to only contain the spine, its parent (i.e., dendrite) andconnected components (Fig. 9b). Then, he switched back to a detailed
view of the spine and the connected components (Neurite L0). For
displaying the astrocytic coverage, the scientist used the proximity
splatting technique in Abstractocyte (Sec. 7.2) (Astrocyte L0). At thispoint, the scientist was able discover that the selected spine was actually
making a synapse with a bouton that had been missed in the labelingprocess (Fig. 9c), which explained the high astrocytic coverage of thespine. Subsequently, the scientist was able to go back to the original
volume data to fix the segmentation of the missed synapse and bouton.
8.3 Case Study 2: Analyzing Glycogen Distribution
In this second case study, our collaborators investigated how glycogengranules are related to boutons and spines (i.e., the locations on neurites
where synapses are located). Glycogen granules in astrocytes areenergy reserves and are used particularly during sustained activity.
Therefore, our collaborators wanted to look at glycogen granule clusters
in relation to their closest synapses. By looking at the detailed glycogendistribution and proximity to synapses, they hope to be able to answer
the following questions: Does the glycogen distribution correlate withthe location of synapses? Does the size of a glycogen cluster correlatewith some synapse attributes (e.g., the strength of a synapse)?
Our collaborator wanted to use the same dataset as in the previous
case study, so he was already familiar with the general morphologyof the astrocyte and neurites. To analyze the glycogen distribution, he
first looked at the detailed toon-shaded astrocyte mesh (Astrocyte L1),and enabled the rendering of individual glycogen granules as small

Fig. 10. Case study 2: Glycogen distribution. Glycogen is clusteredand mapped to the closest boutons and spines or mitochondria. The
node-link diagram (i.e., neurites and synapses) shows the glycogendistribution overlayed as a heatmap; (a) The user selects a bouton witha high value of glycogen mapped to it, and filters the graph based on theconnectivity of the selected bouton. The 3D rendering shows that thebouton (magenta) forms two synapses with two different spines (gray),and therefore hypothetically requires more energy; (b) Glycogen mapped
to neuronal-mitochondria. The darker, the more glycogen is mapped
onto a mitochondria; (c) The same mitrochondria belongs to a dendrite(yellow) forming five synapses (black) with different boutons (teal).

distribution of mitochondria within the astrocyte. His questions wereabout how much of the astrocyte’s body has mitochondria in it, andwhether mitochondria are distributed evenly or, for example, skip finestructures. In this specific dataset, our collaborator could not identify
any mitochondria in fine branches of the astrocyte. However, there weresome regions within the astrocyte where mitochondria were clustered.
Therefore, he explored that area in more detail, especially the synapses
close to that area. For this, he filtered the data using the proximity
filtering feature (Sec. 7.2) to the astrocytic-mitochondria. Next, heswitched to a node-link diagram (Neurites L4, 2D) of the filtered data,
and was able to discover a spine with four synapses to four different
boutons, see Fig. 4 (1). Fig. 4 (left) shows how the scientist was
navigating inside our abstraction space panel during this last part of thecase study. Thus, our collaborator switched back to the 3D rendering(Astrocyte L3, Neurite L0), and could identify a handful of synapses
connected to one spine that was in very close proximity to an astrocyticmitochondria. It is believed that mitochondria of astrocytes can buffer
calcium which, in turn, can be released and used to signal and regulate
synapse activity. Our collaborator speculated that this might be the casefor the identified synapses, and wants to further investigate this.
9

spherical objects within the mesh. Next, he first visualized the distanceto the glycogen granules on nearby spines and boutons (Neurite L0).
After getting a first overview of the glycogen distribution, he performed
on-the-fly clustering of the granules and mapped the resulting clusters
to their closest spines and boutons. In Abstractocyte, we can visualize
this by computing the volume of the glycogen clusters mapped to thesespines/boutons and color-code the surface or node accordingly. Next,
our collaborator sorted the resulting spines and boutons by their mapped
glycogen volumes and selected the top result (Fig. 10a). Upon closer
inspection of the identified bouton, our collaborator discovered thatthe bouton makes two synapses with two different spines, respectively(Fig. 10a). Hence, he continued to investigate the astrocytic coverage ofthe bouton (similar to case study 1), and determined that the astrocyte
wraps around both of these synapses for support. This indicates that
boutons that make two synapses might require more energy, which ourcollaborator now wants to investigate in more detail in the future.
In the final step of this case study, our collaborator was interested in
how the glycogen distribution is related to neuronal-mitochondria. To
investigate this, he performed the same initial steps as above but mapped
glycogen clusters to their closest mitochondria (Fig. 10b). Among themitochondria that had the highest coverage of glycogen, he was able
to identify a mitochondria that belonged to a dendrite that made fiveconsecutive synapses on its shaft, indicating that the operation of these
synapses also requires a lot of energy in a small spatial area (Fig. 10c).
Prior to Abstractocyte, our collaborators had to resort to the generic
Blender 3D rendering software for rendering high-quality meshes of
the astrocyte and glycogen granules [10]. They had no way of easily
analyzing the distance of glycogen granules to their closest synapses,and could not interactively filter their data or switch between different
visual representations. Using Abstractocyte, the scientist was able toquickly identify relevant neurite structures and further analyze them.
8.4

Case Study 3: Astrocytic-Mitochondria Coverage

Mitochondria are the site of the cellular respiration process, and are
responsible for providing energy to the cell. In astrocytes, however,mitochondria can also buffer calcium, thereby influencing intracellular calcium signaling, and potentially regulating synapse activity.
Therefore, our collaborator wanted to use Abstractocyte to explore thelocation of astrocytic mitochondria in relation to synapse locations.
Mitochondria tend to appear in clusters or microdomains in astrocytes.
Therefore, the scientist also wanted to explore the intracellular distribution of mitochondria within astrocytes.
In this case study, the scientist first looked at the astrocyte in askeleton representation (Astrocyte L2), with the mitochondria mapped
onto the surface of the skeleton. This allowed the scientist to quickly see
the morphology of the astrocyte with highlighted locations indicating
mitochondria. This way, our collaborator could look at the actual

D ISCUSSION

During the case studies we also collected qualitative feedback fromour collaborators. Overall, the scientists felt that Abstroctocyte offersa level of flexibility to their analysis and exploration tasks that they
did not have before. They moved freely between different abstractionlevels and also regularly used interpolated abstraction values, e.g.,
semi-transparent surface meshes with underlying skeletons.
They outlined three main advantages: First, because Abstractocyte
allows displaying neurites and astrocytes at the same time, but withdifferent abstraction levels, it enables users to preserve the context ofone, while investigating details of the other. For example, keeping askeleton of the astrocyte while investigating neurites and synapses helps
to see the relation of the astrocyte to the current structures of interest.
Second, the proximity visualization allowed our collaborators to get
an immediate impression of which structures are close to a structureof interest. Together with the proximity filtering feature, this allowed
them to investigate those structures right away. Third, our collaborators
were very impressed with the immediate visual feedback during theirglycogen analysis case study. The immediate visual feedback sped up
their analysis, and allowed them to explore several different hypotheses
without having to dive into more elaborate statistical computations rightaway. Within just the first hour of using Abstractocyte, our collaborators
were able to discover things they had not known about their data before.
Limitations. The main limitation of Abstractocyte with respect toanalyzing large EM volumes of astrocytes is that our collaborators haveso far only collected datasets containing a single astrocyte. Therefore,
Abstractocyte currently does not support filtering for specific astrocytes,
or the visual comparison of multiple astrocytes. However, supporting
multiple astrocytes in a single dataset is straightforward to add in
the future. Another limitation with respect to analyzing EM brain
volumes is that our current system focuses on the visual analysis aspect.
While we do provide the most important quantitative measures, e.g., forclusters, proximity, or coverage, our focus is not on replacing a fullyfledged statistical analysis. On the contrary, we see Abstractocyte as
the tool for neuroscientists to quickly explore initial hypotheses about
their data before they continue to a more detailed statistical analysis.
10

C ONCLUSIONS AND F UTURE W ORK

Abstractocyte allows neuroscientists to visually explore astrocytes andneurites at different levels of detail. It enables scientists to intuitively
choose the visualization that is most appropriate for their current analysis task, by offering an easy-to-use user interface that allows themto explore all possible combinations of abstractions. We think that bygiving users the power to easily switch between abstraction levels in acontinuous way, we encourage them to explore their data in more detail.
By using smooth transitions between different abstraction levels we canpreserve the visual context and correlations throughout the transitions,enabling users to stay focused on their current task, while still providing

the necessary contextual information. In addition to dynamic visual
abstractions, we have incorporated methods for the visual analysis ofglycogen granules and mitochondria that allow a more detailed analysisof astrocytes in relation to their neighboring neurites.
We believe that the abstraction space introduced in this paper can begeneralized and applied to different visualization problems in a wide
variety of domains. In the future, we would therefore like to work on
allowing other scientists to explore the space of possible visualizations
that is suitable for their domain and their specific kind of data.
ACKNOWLEDGMENTS
This work was supported by funding from King Abdullah Universityof Science and Technology (KAUST) and KAUST award OSR-2015CCF-2533-01.
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