Volume 41 (2022), Number 3

Eurographics Conference on Visualization (EuroVis) 2022
R. Borgo, G. E. Marai, and T. Schreck(Guest Editors)

Barrio: Customizable Spatial Neighborhood Analysis andComparison for Nanoscale Brain Structures
Jakob Troidl1,3 , Corrado Cali2 , Eduard Gröller3 , Hanspeter Pfister1 , Markus Hadwiger4 and Johanna Beyer1
1 Harvard University, 2 University of Turin, 3 TU Wien, 4 KAUST

Figure 1: Customized scalable comparison of spatial neighborhoods. Barrio supports user-defined analysis scenarios (a,b) to automaticallyadjust visualizations, view parameters, and the user interface for the comparative analysis of nanoscale brain structures. Barrio suggests thecomparison method that is best suited for the number of user-selected neighborhoods that are being compared in the analysis view (f). Users
first specify the biological target of their analysis (a), and define preferred visualization methods for comparisons of different cardinalities(i.e., for two to n structures) (b). Barrio automatically adjusts the visualizations and UI, including the settings panel (c), a list view of
instances of a target type (d), the 3D overview (e), the quantitative analysis views (f), and the detailed 3D neighborhood views (g).
Abstract
High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function anddevelopment through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to
be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations.
We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, andsupported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparativevisualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures
to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges themin linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualizedvisualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis inneuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
CCS Concepts• Human-Centered Computing → Spatial neighborhood analysis, Visual comparisons, Neuroscience, Scientific visualization;

1. Introduction
Neurobiologists are acquiring ever-increasing amounts of highresolution image data of the mammalian brain, aiming to tackle
questions regarding brain physiology, the onset of diseases, and the© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John
Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

emergence of consciousness. Connectomics researchers are interested in the detailed connectivity between neurons at nanoscale
resolution [LD11], as well as in the analysis and comparison of
local spatial neighborhoods around neuronal structures and cell organelles. However, mammalian brains are staggeringly complex,

J. Troidl / Barriowith tens of millions of interconnected neurons, dozens of cell substructures (i.e., cell organelles) per neuron, and billions of synapses.
Thus, the number of interesting neighborhoods and comparisonsof neighborhoods in such a dataset is even higher, ranging fromdetailed pair-wise comparisons to the comparison and clusteringof dozens, hundreds, or thousands of structures. For example, toevaluate how spatial variations in neuronal tissue might influence
brain development and neuronal connectivity, scientists need tocompare different biological structures (such as synapses), analyze
distances and spatial correlations (e.g., between synapses and cellorganelles), and look at how certain biological features are spatiallydistributed. The specific details of this analysis (e.g., the biologist’s
goals, comparison target types, measurement types) vary for eachscientist. While one expert might be interested in the distributionof mitochondria within nerve cells, others might only focus on thespatial neighborhoods of synapses. Therefore, in this work, we havedeveloped a framework that supports a highly customizable spatial
neighborhood analysis workflow of scientists and a flexible comparative analysis, which scales to their data sizes.
Most visualization tools for connectomics provide either 3D
views for exploring large, segmented electron microscopy (EM)volumes [BAAK∗ 13, BHAA∗ 13, HBJP12], or focus on the analysis of synaptic connectivity [AABS∗ 14, SBS∗ 13]. Little work has
focused on the scalable visual comparative analysis of well-definedcell neighborhoods in EM data. Furthermore, while all of thesetools target neuroscientists, none of them can adapt to the highlyindividualized and specific domain goals that neuroscientists often
have. Most tools designed to be general enough to support several
types of analyses result in complex user interfaces and unintuitive
workflows. On the other hand, tools designed for a specific analysis
task typically do not generalize well to other tasks, often summarized as the expressivity vs. usability trade-off [FCOO12].
In this design study, we present Barrio, a novel tool for the interactive analysis of spatial neighborhoods in high-resolution 3D electron microscopy (3DEM) data. In particular, we focus on neighborhoods around cell organelles. The main contribution of this work
is a customizable framework for spatial neighborhood analysis of
3DEM data, facilitating the analysis of spatial distributions, distances, and 3D morphology. Scientists can specify new, individualized analysis scenarios and appropriate visualizations that support
their analysis tasks. Second, we contribute a scalable visual comparison method for spatial structures that allows the comparisonof a wide range of element cardinalities, e.g., ranging from two tohundreds of structures within a single comparison. Third, we report on a detailed case study of using Barrio to analyze the spatial
neighborhood of mitochondria, the novel visual encodings we designed for it, and the insights gained by the scientist. Finally, we
also report on a qualitative user study with eight domain experts.
2. Related Work
Visualization for Connectomics. Visualization approaches havebeen applied to the entire neuroscience and connectomics dataaquisition and analysis pipeline [HHM∗ 17], ranging from segmentation [BSL18, GG90] and visual proofreading of segmentations [AABH∗ 16, HKT∗ 18, GWB∗ 21] to data exploration andanalysis [BAAK∗ 13, AABS∗ 14, MAAB∗ 18, CBB∗ 16]. Data ex-

ploration typically focuses either on the original large-scale microscopy data and its segmentation, or on higher-level connectivity information [PKB∗ 12] and uses tools that are geared towards the neuroscience workflow and data rather than generalvisualization systems. Structural connectivity analysis of neuronal cells often relies on abstract visual metaphors such as subway maps [AABS∗ 14] or circuit diagrams [SBS∗ 13] that highlight synaptic connections. On the other hand, exploring the general morphology of the data typically relies on scalable volume
visualization of the original or segmented electron microscopy
data [HBJP12, BHAA∗ 13]. Few works have focused on the visual analysis of local energy consumption [ACA∗ 19] and interactions between cell organelles [MAAB∗ 18]. However, none of theseapproaches analyze neighborhoods of selected neuronal structures
and also do not support comparative visualizations of them.
Visualization Specification and Generation Frameworks.
Frameworks for the specification and generation of visualizations can be classified into low level [BOH11, BH09] and highlevel [SWH14, SH14, SRHH16, SMWH17] approaches, based on
the level of abstraction they provide in their interface. Many techniques use declarative methods (i.e., defining what not how) to
specify data visualizations. Low-level visualization specifications
like D3 [BOH11] require users to compose a visualization programmatically by specifying its components, while high-level approaches may support interactive data binding and visualization
specification using drag and drop [SH14]. Barrio is inspired bythese approaches, but offers an interface for domain-specific visualization specification. Users can define domain-specific analysis scenarios and map different visualizations to them. Our systemparses this input and automatically creates customized views.
Spatial Neighborhood Analysis. Identifying and searching forpatterns in cell neighborhoods is an active area of research. For
highly multiplex imaging data, tools such as CytoMap [SFG∗ 20]
and Facetto [KBJ∗ 20] have been proposed, which support the
search for visual patterns in cell neighborhoods and hierarchial clustering of cell types for phenotype analysis, respectively. Other approaches focus on cell clustering based on Hidden
Markov random fields concerning gene expression and localization
data [Pet15], phenotype analysis for cellular screens [DSG∗ 17],
analysis of cell interactions [NDBB17], or the evaluation of patterns in cell populations [FGBHOdS05]. All of these approaches
use multiplex imaging or microscopy data at a much lower resolution than EM data and, therefore, focus on larger cell clusters
and areas. Jorstad et al. [JNC∗ 15] allow users to manually extract
quantitative measures from reconstructed meshes of cell organellesin high-resolution EM data. However, they do not focus on neighborhood analysis and comparisons between organelles.
Comparative Visualization. Comparative visualization allows
users to asses similarities and differences between multiple
data points or data sets. Visual comparisons can be classified into juxtaposition, superposition, and explicit encoding ofdifferences [GAW∗ 11], or into data-level and image-level approaches [PP95]. Comparing many structures simultaneously requires scalable visualization methods and encodings, such as forlarge genome matrices [LBK∗ 18] or trees [MGT∗ 03], or the combination of spatial and non-spatial features [MMH∗ 13]. We use© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

J. Troidl / Barrio
Neuron:Mitochondrion

Dendrites
Cell
body
Axon

Ribosomes

Dendrite

Spine
SynapseAxon

4. Goal & Task Analysis

BoutonFigure 2: Structure of neurons and cell organelles. Nerve signals
are transmitted from axons to dendrites via synapses. Synapses often form between a presynaptic bouton and a dendritic spine. Neurons contain cell organelles, such as mitochondria or ribosomes.

Following a problem-driven design study approach [SMM12], wehave identified domain goals, associated analysis tasks, and general system requirements in semi-structured interviews with four
experienced neuroscientists at the University of Turin and the Harvard Center of Brain Science. All scientists work with 3DEM dataand have more than ten years of experience in the field. Over thecourse of several months, we regularly met with our main collaborator (who is also a co-author) to iterate on design prototypes.
4.1. Domain Goals

juxtaposition and superposition to compare neuronal structures andspatial neighborhoods in a scalable way, both in spatial and nonspatial views. For comparing scalar and flow fields, contour trees
and graph matching have been used [HSKK01, SWC∗ 08], as wellas graph comparison for analyzing brain connectivity [ABHR∗ 13].
For 3D structures, Schmidt et al. [SPA∗ 14] compare surface meshes
against a reference mesh. For our data, reference meshes do not exist due to the variability in neurite shapes and branching patterns.
Thus, we compare structures in both, 3D and data analysis views.
3. Background in Neuroscience
Neuroscience Fundamentals. The human brain consists of billions of interconnected nerve cells, or neurons. Neurons receive input on tree-like structures called dendrites and transmit signals via
long tubular structures called axons (see Fig. 2). Axons and dendrites (collectively called neurites) connect via synapses to neighboring cells [Stu08]. The presynaptic areas of axons are called
boutons, while the postsynaptic protrusions of dendrites are called
spines. In addition to synaptic connectivity, subcellular structuresof neurons, so-called cell organelles, are also of high interest toneuroscientists. Mitochondria, for example, are cell organelles responsible for providing energy to the cell and its synapses [SW06].
They vary significantly in their shape, size, and location within acell. Our approach studies the spatial neighborhoods of these structures, to gain insights into how they mutually influence each other.
Connectomics Workflow and Data. Our collaborators start dataacquisition by dissecting solid blocks of brain tissue stained forelectron microscopy. Next, they image individual slices with anelectron microscope (EM) at a pixel resolution of 5 × 5 nanometers [KMWL08], with a slice thickness of 15 nanometers, and register them into a 3D volume. Scientists segment neurites, synapses,
mitochondria, spines, and boutons using semi-automatic tools like
Ilastik [SSKH11], TrackEM [CSS∗ 12], or VAST [BSL18]. Additionally, they measure distances between those structures (details
in Sec. 7). As with any imaging method, EM provides a snapshotof a current physiological state that might change over time. However, it still provides a valuable glimpse into the neuroanatomy ofa specimen. Our collaborators have collected and reconstructed six
data sets of layer one of the somatosensory cortex with a focusedion beam scanning electron microscope (FIB-SEM), which are all
five cubic microns in size. Three data sets show brain tissue of fourmonth-old mice, while the other three show tissue of 24-monthold mice [CWB∗ 18]. Each dataset contains roughly 900 segmented
structures (400 neurites, 300 cell organelles, and 200 synapses).
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Our collaborators’ main objective is the detailed analysis of local
neighborhoods in their neuronal 3DEM data sets. In particular, theywant to analyze, compare, and correlate morphological features ofinterest in selected cells, such as spines, boutons, or mitochondria,
to discover previously unknown biological patterns. Interactive exploration is crucial, as our collaborators need to investigate the detailed 3D morphology to recognize subtle patterns or areas of interest that cannot be easily detected with automatic methods. The
particular domain-specific goals of the neuroscientists are:
G1 - Proximity Analysis for Cell Organelles. The collaborators
are concerned with the spatial aspects of how cell organelles relate
to each other. On the one hand, they are interested in analyzing distances between a varying number of structures (e.g., What are thedistances of the closest three synapses to a mitochondrion?). On theother hand, they are interested in distributions (e.g., What percentage of a mitochondrion is closer than 30 nm to the cell boundary?).
Combined with 3D spatial exploration of the dataset, our collaborators want to investigate whether there are any patterns that indicate
connectivity strength based on the underlying spatial arrangementof cell organelles. Our initial interviews showed that distances anddistance distributions are important as a metric for neuroscientists,since the influence of cell organelles and synapses is spatially restricted to their immediate neighborhood.
G2 - Comparative Analysis of Neuronal Structures. In addition
to looking at a single spatial neighborhood, our collaborators needto compare neighborhoods and structures of interest. Their goal isto perform comparisons with different biological targets, to compare between: a) different data sets, b) different instances of a structure of interest (e.g., dendrites), or c) different attributes of a single
structure of interest (e.g., synapse distances to a single mitochondrion). Additionally, they need to be able to perform comparisonsof different cardinalities, ranging from a detailed comparison of
two structures to comparing hundreds of structures at the sametime. This feature allows them to test whether local findings also
apply to a larger data set and, conversely, allows them to drill downfrom a larger perspective to highly-detailed local instances.
G3 - Customizable Analysis of User-Specific Hypotheses. Ourscientists want to fine-tune their visual analysis to their current scientific objective. In addition to general comparisons and proximity
analysis in their data, they want to customize analyses steps anddefine which visualizations to use for the domain-specific tasks.
Therefore, their system should be easily customizable to allow fordifferent views, UI elements, and interactions, and guide the users
through their analysis based on their scientific objectives.

J. Troidl / Barrio

4.2. Analysis Tasks
Based on the above goals, we have derived a set of analysis tasks
scientists need to perform:
T1 - Explore original EM image data and segmented structures.
Before diving into a detailed analysis, scientists first need to get an
overview and verify the correctness of the segmentation (G1).
T2 - Explore the spatial neighborhood of a single structure.
Once a structure of interest has been identified, domain scientists
explore its spatial features and 3D morphology (G1, G3).
T3 - Quantify single distances and distance distributions ina spatial neighborhood. After their initial exploration, scientists
need to analyze proximity and distances more accurately (G1, G3).
T4 - Compare the spatial neighborhoods of several structures.
Analyzing the variability of certain features in different local spatial
neighborhoods is essential for discovering trends and patterns whenstudying biological features (G2, G3).
T5 - Compare neighborhoods of different data sets. Scientists
want to compare different specimens to analyze the stability of certain neighborhood features and differences between them (G2, G3).
4.3. Higher-Level Requirements
To support the domain goals and analysis tasks for many different users and their individual domain questions, we have extracted
some higher-level design requirements that Barrio must fulfill:
R1 - Flexibility and Expandability. While all of our collaborators want to analyze and compare spatial neighborhoods, the details vary for each scientist. For example, one scientist is interested
in mitochondria, another one in endoplasmatic reticula. To support
both (and other) usage scenarios, Barrio has to be customizable andexpandable to allow scientists or technicians to adjust the provided
visualization capabilities for specific domain questions (G3).
R2 - Scalable Comparisons. To support the visual comparison ofdifferent biological targets and at different cardinalities, our systemneeds to provide comparisons at different scales and allow users toswitch between them easily (G2).
R3 - Ease of Use and Automatic Guidance. In addition to being
flexible and expandable, the system must also support neuroscientists with little or no programming experience. Biological data isinherently complex. Therefore, we need to limit the cognitive load
for users by employing methods that automatically guide and support them in their data exploration. Hence, Barrio should provide
a human-readable interface and file format for customization andautomatically guide users in their specific analysis tasks (G1-3).
R4 - Shareable Analysis Configuration. After discovering an interesting observation in a data set, experts often want to recreate
their results later or share them with colleagues. Results often rely
on complex user settings that are hard to reproduce in other environments. Hence, Barrio should support saving and sharing ofuser-specific analysis configurations.
5. Barrio Toolkit Design
The design of Barrio tackles the requirements R1 and R3 by supporting user-defined analysis scenarios and subtle automatic guid-Figure 3: Barrio workflow and automatic user guidance. Barrio automatically initializes and updates the user interface, adjusts
views and view parameters, and adapts the level of detail (LOD)for visual neighborhood comparison based on the user-specified
analysis definition, while the user interactively explores the data.
ance throughout the analysis. Based on the custom user-defined
analysis scenario, Barrio suggests fitting visualizations, adjusts
view parameters, and offers different comparison modes. Sec. 6 describes our visual neighborhood comparison approach (R2).
5.1. User Workflow
The user starts by loading a custom analysis scenario and its data.
The specification of the scenario is done prior to the interactive
analysis (see Sec. 5.2). Users perform an initial exploration in 3D
and select structures of interest for further investigation. Next, users
can perform a detailed analysis, such as comparing user-selectedstructures in detail to extract distances, detect distribution patterns,
or test hypotheses. In all these steps, users are supported and guided
by Barrio to streamline the analysis process and minimize themental load. For instance, Barrio selects appropriate visualizations
based on the current data cardinality or displays 3D views of thespecified neighborhood automatically. Fig. 3 shows the user workflow and which parts of the framework are adjusted automatically.
5.2. Custom Analysis Scenarios
Barrio allows users to define custom analysis scenarios that finetune the general neighborhood analysis (R1).
Scenario Specification. Conceptually, a scenario defines the analysis a neuroscientist wants to perform and breaks it down into individual and concrete scenario subtasks (see Fig. 1a). Each scenario
subtask is defined in terms of data mappings, default visualizations,comparison modes, and supported user interactions. We will referto the tasks in a user-defined analysis scenario as scenario subtasks
to distinguish them from the general analysis tasks identified in ourgoal and task analysis in Sec. 4. Below, we describe the concreteelements comprising a scenario.
Scenario: An analysis scenario represents a higher-level question a specific domain scientist might have, such as “How do mitochondria influence their spatial neighborhood?”.
Scenario Subtasks: Each analysis scenario comprises one or several scenario subtasks, which are specific analysis steps a userneeds to perform to answer the higher-level scenario question. For
example, a scenario subtask in a mitochondria analysis scenario
would be to analyze the synaptic neighborhood of a single mitochondrion, i.e., synapses in close proximity to the mitochondrion.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

J. Troidl / Barrio

JSON based format allows domain experts to easily store, edit,
and share analysis configurations, which supports the reproducibility of their analysis results (R4). We provide sample ADFs thatusers can further fine-tune. Barrio’s documentation includes detailed information about supported visualizations and their parameters [VCG22b].
5.3. Automatic User Guidance

Figure 4: Structure and example of an analysis definition file(ADF). The ADF defines analysis scenarios, scenario subtasks, target types, visualizations and UI parameters.
To support these steps, each scenario subtask can define visualizations, comparison modes, and interaction parameters that help theuser in the analysis. A high-level view of a scenario, its subtasks,
and visualization specifications is shown in Fig. 4.
Target type: Each scenario subtask can define a target type,which refers to the type of biological structure that is under investigation for the task. Target types can be any biological structures thathave been segmented or labeled in the original EM dataset. Notethat the target type refers to a biological class (e.g., all synapses)and not to a specific instance of that class (e.g., synapse x). During
the interactive analysis, users can then select specific instances of
the target type for further investigation. Defining a target type ensures that visualizations and user interface elements automaticallyfocus on that type (e.g., show a list of all instances of that type, or
automatically center 3D views on those instances).
Visualizations and data mappings: Each scenario subtask can
specify customized visualizations that are best suited to complete
the task. Barrio supports a set of standard information visualization views, such as bar charts, scatterplots, or violin plots, as well
as several custom information visualization views for neurosciencetasks. Each view can be customized by changing the specific data
mapping, such as which attributes to show on the axes of a scatterplot. For example, to see information about the proximity of astructure to the nearest cell membrane, we can color code the distance onto the surface of the structure, or show a violin plot of thesame data. Furthermore, Barrio is extensible and allows to implement and plug in additional views that are not part of the originalframework (see Sec. 7). Finally, we can define comparative viewsfor different data cardinalities (i.e., different numbers of objects
in a comparison). This means that we can specify a high-detailed
view for low-cardinality comparisons, and overview visualizations
for high-cardinality comparisons (R2). We describe our scalable
comparison approach in Sec. 6.
Interaction parameters: In addition to specifying visualizations
and tasks, a scenario can also define interaction parameters, such as
user-adjustable sliders for filtering data prior to visualization.
Analysis Definition File. Custom analysis scenarios are definedby users in the form of analysis definition files (ADFs), as shown in
Fig. 4. ADFs are written in a human-readable JSON format anddo not require programming skills (R3). Barrio reads the ADF
upon startup and configures the user-interface accordingly. The© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Based on the analysis scenario defined in the ADF, Barrio automatically adjusts the user interface, views, and interactions to help
experts in their analysis (R3). Our goal is to minimize the users’
mental load during the interactive analysis, so that they can focus
on their domain science rather than on operating the analysis interface. Barrio includes the following guidance features (see Fig. 3):
User Interface Initialization: Barrio automatically adjusts the userinterface based on the user-specified analysis scenario in the ADF.
This includes updating the view arrangement and adding necessary
user input elements such as sliders or checkboxes. For example, foranalyzing mitochondria, scientists might want a slider to interactively change the distance threshold of what they consider a “close”
synapse. Furthermore, we automatically show preview icons of thethree different comparison levels for each scenario subtask (seeFig. 1c). The icons allow users to choose their current scenario subtask quickly and to switch comparison mode manually, if desired.
View and View Parameter Adjustment: Barrio automatically updates the list of available visualizations and adjusts views and viewparameters whenever a new scenario or scenario subtask is selectedby the user. For example, whenever a scenario defines biologicaltarget types (e.g., mitochondria), we automatically highlight all instances of that type throughout the system. We show all instances ofthat type in the list view and blend out other structures (see Fig. 1d).
Additionally, spatial neighborhood views and every spatial neighborhood analysis will automatically be centered around the specified target type. When users switch between scenario subtasks withdifferent target types, the views and UI adjust accordingly.
Level of Detail for Visual Comparisons: Barrio can automaticallyadjust the level of detail in our scalable comparison approach, depending on the number of structures selected for comparison (seeSec. 6). This allows us to adjust the level of detail and abstractionlevel in a comparative visualization based on data cardinality.
6. Visual Neighborhood Analysis
Analyzing and comparing spatial neighborhoods is challenging, especially in biological tissue that is highly variable and exhibits acomplex three-dimensional structure. Looking at not just one but
multiple neighborhoods solely in 3D, easily leads to cognitive overload and does not provide quantitative comparisons. Therefore,
Barrio offers two complementary visualization methods. First, we
can show juxtaposed high-detail 3D renderings of the selected local
neighborhoods. This allows neuroscientists to explore the original
3D data and get an intuition about the morphology and spatial arrangement of interesting structures. In addition, we have developed
a comparison approach for the linked analysis views that scales
from small to large comparisons and that supports a more quantitative analysis of neighborhoods, described below.

J. Troidl / Barrio

We define a spatial neighborhood as a sub-volume centeredaround a chosen structure of interest (e.g., a mitochondrion). By default, the neighborhood contains the structure of interest and its surrounding cell (see Fig. 6). Alternatively, the user can determine thesize of a neighborhood by interactively adjusting a distance threshold with a maximum of 6 microns around a structure of interest.
6.1. Scalable Comparison Approach
Our comparison method supports two levels of comparison, orthogonal to each other. On the one hand, we support different biological target types. On the other hand, we support comparisons ofdifferent cardinalities (i.e., a varying number of objects). Both approaches together allow comparisons to range from overview to detail in a variety of different usage scenarios (R2). The scalable comparisons are integrated into the custom analysis scenarios, where
Barrio suggests and adjusts comparative views based on the current scenario subtask and the number of selected structures.
Comparison Targets. Barrio supports comparing different instances of the same biological target type (e.g., comparing two dendrites). It also supports the comparison of cellular substructures ofthe same structure of interest (e.g., synapses of the same dendrite).
In addition, Barrio realizes the comparison of different data sets
(e.g., data sets of neuronal tissue in different developmental stages)(see Fig. 9). Users can define comparison targets in the ADF bysetting the target type and specifying the data mapping for comparisons. Barrio then automatically adjusts the UI and views based on
the specified comparison target.
Cardinality of Comparisons. In their analysis, scientists can movefrom a single interesting structure to many, to verify whether identified features correspond to trends or patterns. Alternatively, they
can start with an overview of many structures and then drill down
to see more details. A highly detailed comparison takes up more
screen space and only supports a low number of objects. Comparing a high number of objects would not be ideal with a high-detailcomparison view. We have designed a scalable comparison approach to support comparisons of sets with different cardinalities,
and have integrated our method into Barrio. In particular, Barrio
provides comparisons at three different scales, as shown in Fig. 5.
In our visual design, we use either a single visualization, a single visual element, or a single visual mark per neighborhood for the low-,
medium-, and high-cardinality views, respectively. This follows theintuitive notion of adjusting the amount of data we show per neighborhood, depending on the number of neighborhoods we compare.
The low-cardinality comparison mode is designed to compare ahandful of objects (two to five) and offers detailed juxtaposed viewson the objects being compared, arranged as small multiples. The
medium-cardinality comparison mode supports up to a dozen structures, while the high-cardinality comparison view scales up to hundreds of objects. The latter view is an overview visualization andmakes trends, patterns, and outliers visible.
Barrio can either automatically switch between comparison
modes, depending on the number of selected objects, or let users
choose the desired comparison mode. The actual visualization foreach comparison mode is based on the scenario subtasks specified
in the ADF. Fig. 7 and Fig. 8 show the different cardinality views
for the analysis scenario described in Sec. 8.

Figure 5: Scalable comparisons at three different cardinalities.
Low-cardinality comparisons (top row) analyze a small numberof spatial neighborhoods and use juxtaposed small multiples.
Medium- and high-cardinality comparisons support increasingly
larger numbers of neighborhoods and encode a single neighborhood as a single visual element (i.e., a violin in a violin plot) or asingle visual mark (i.e., a dot in a scatterplot), respectively.
6.2. Visual Elements
Next, we describe the visual elements of our research prototype(see Fig. 1) and how they support visual neighborhood analysis.
3D Neighborhood View. The neighborhood view (see Fig. 1g) is
the starting point of many analyses and examines the local spatial
neighborhood of a user-selected structure of interest in detail (T2).
Neuroscientists can analyze the morphology and three-dimensional
anatomy of the selected structure while at the same time seeing
nearby cellular substructures. Detailed three-dimensional visualizations of neighborhoods are essential for understanding the spatial arrangements and also provide context for the abstract analysis views, which quantify neighborhood features. For example,
in the neighborhood view, we can show a selected mitochondrion,
its surrounding dendrite, as well as nearby synapses. We display
the selected structure of interest and the surrounding region in ahigh-resolution 3D surface rendering and support different rendering styles (see Fig. 6). We support focus and context rendering by
displaying surrounding neuronal structures as silhouettes to highlight cell organelles located inside a cell. In addition, users can enable a slice view of the original EM data and move it through theneighborhood view (T1). With the slice view users proofread segmentation accuracy on-the-fly. They can analyze features in the EMdata that have not been segmented and reconstructed yet, such as
the distribution of individual glycogen granules.
We have implemented the neighborhood view as small multiples(see Fig. 1g). We juxtapose views of multiple local neighborhoods
next to each other (T4, T5). Small multiples can be linked to eachother for a synchronized navigation between all views.
Spatial Context View. This view provides an initial overview ofcurrently selected neighborhoods and all its segmented structures(see Fig. 1e). Scientists can see the spatial location of selectedstructures in the context of the entire volume and they can examine
how different local neighborhoods are related to each other, andwhether they share synapses or exhibit clusters of certain structures (T1). The spatial overview is linked to all other views so thathovering over a structure in the neighborhood view highlights therespective structure in the spatial context view and vice versa.
Analysis View. While the 3D neighborhood views are great for© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

J. Troidl / Barrio

marily used by scientists to look at new datasets that they have notanalyzed before. Alternatively, users can select individual neighborhoods in the tree list view and look at them in small multiple
3D neighborhood views before starting a more quantitative analysis. This mode is particularly useful if scientists already have aneighborhood or region of interest in mind that they want to investigate. To further aid the user in finding interesting neighborhoods,
when selecting one structure in the tree list view, we automaticallyhighlight spatially close structures in the list view as well.
Figure 6: Detailed 3D neighborhood view. The dendrite (green)and close synapses (red). (a) 2D slice overlay of the original EM
data to show biological context. (b) Silhouette enhancing, andsemi-transparent surface rendering to highlight how enclosed mitochondria (blue) are positioned within the dendrite. (c) Colorcoding of a mitochondrion showing the distance between the mitochondrion and its surrounding cell membrane.
getting a spatial understanding of a neighborhood, it is still hard
to compare multiple neighborhoods in a more quantitative way.
Therefore, Barrio offers analysis views (see Fig. 1f) that abstract
certain neighborhood characteristics and support direct comparisons between neighborhoods (T3, T4, T5). Fig. 5 shows examples
for the different chart types in Barrio, such as violin plots, or scatter plots. A complete list is given in the documentation [VCG22b].
The actual visualization in the analysis view is based on the numberof selected neighborhoods (i.e., the cardinality of the current comparison) and on the specific analysis scenario. Analysis views and3D neighborhood views are connected by brushing and linking.

7. Data Processing and Implementation
Barrio Architecture. Barrio is a modular application that separates
the Barrio-core from the user interface and the visualization stack.
The visualization stack provides a general API for all visualizations in our system. Whenever a new analysis scenario is loaded,
the core module re-initializes and updates the user interface anddifferent views of the visualization stack, as specified in the ADF.
For flexibility, Barrio’s visualization stack is extensible. Additional
D3-based data visualization modules have to implement our visualization interface and can then be further customized via the ADF.
Since Barrio already supports standard visualizations (e.g., scatterplots, bar charts, etc.), this feature is primarily meant to support
novel custom visualizations specific to a particular usage scenario.

Linked 3D Navigation: To facilitate fast and intuitive visual analysis of multiple neighborhoods, users can link zooming and navigation for all 3D neighborhood views.

Data Preprocessing. We display surface meshes of the reconstructed structures as well as the original EM image volume. Therefore, we use a standardized file format [VCG22b] to store surface
meshes, imaging volumes, any manually labeled meta data, andautomatically computed measures such as distances. We extract
meshes from human proofread segmentation volume with Neuromorph [JNC∗ 15] and store them in wavefront format [Wav21].
Synapses are stored as meshes of the segmented postsynaptic densities. Additionally, labeled mesh attributes provided by domainscientists, such as spine lengths, spine volumes, or surface areas
are stored in JSON format and can be used as visualization parameters in the ADF. Finally, we pre-compute and store the closest distances between neuronal structures as well as distance distributions
to neighboring structures based on evenly sampled reconstructedsurface meshes using the CGAL library [FGK∗ 00]. To computethe distance between two structures, we use the distance between
their two closest surface points (i.e., minimum distance), as this isof biological significance to our collaborators. To compute a distance distribution for structure A in relation to structure B, we compute the minimum distance of each surface point of structure A to
B. CGAL uses an axis-aligned bounding box (AABB) tree to efficiently compute distances. Since we use meshes generated from human proofread segmentation masks for the distance computations,the distances can be considered accurate. We used a Windows 10
machine with a AMD Ryzen 9 3900X CPU, a GeForce RTX 2080
Super GPU, and 128 GB RAM for preprocessing and for the user
study. Preprocessing 1GB of data takes about one hour.

Neighborhood Selections: Based on initial user feedback, Barrio
supports two main approaches to select neighborhoods for further
investigation. Users can either start with a high-level comparison of
all neighborhoods (using a high-cardinality comparison view). Subsequently they can select interesting neighborhoods based on outliers or clusters in the analysis view. This interaction mode is pri-

Implementation. Barrio is implemented in C++ 17, OpenGL 4.3,
and Qt5. The information visualizations are using D3.js [BOH11]
and Phylotree.js [SWKP18]. We have implemented a state-ofthe-art real-time transparency rendering algorithm using per-pixel
linked lists [Wol11] for the rendering of semi-transparent surfaces,
which requires a current GPU. Barrio is open-source [VCG22a].

Segmentation Tree List View. To easily select structures of interest, we provide a hierarchical tree list view of all segmented objects
in the data set. Scientists can quickly select structures by navigating
the data hierarchically (e.g., from a neuron to its cell organelles).
Alternatively, users can choose to only see the list of objects of thecurrent biological target type (see Fig. 1d), to quickly select structures of interest for their comparative analysis.
6.3. Interaction
Brushing & Linking: Barrio supports brushing and linking between
analysis views, neighborhood views, and spatial context views.
Hovering over a region in the analysis view highlights the corresponding neighborhood view, which is helpful when comparing
many structures. We also highlight structures like synapses or dendritic spines in the 3D views when selected in the analysis viewso that users can connect the insight they gained from the analysis
view to the detailed 3D representation.

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

J. Troidl / Barrio

Figure 7: Analysis views generated for ST1: Synaptic neighborhood of mitochondria. (a) to (c): low-, medium-, and highcardinality comparison views. (a) distance tree for mitochondrion’Mito 1’ showing its surrounding synapses (red) and their closest
mitochondria (blue), (b) bar chart showing three mitochondria andthe spatial distance of their surrounding synapses, (c) distance matrix of all mitochondria and their neighboring synapses.
Scalability. We precompute all distances and store them in a hashtable. Our current datasets contain roughly 1, 100 segmented structures, where each structure is stored as a mesh. For rendering local
neighborhoods, we only need to load the meshes of the structuresinside that neighborhood onto the GPU. Our local neighborhoods,
in practice, contain between 1-30 structures whose meshes need tobe rendered. We currently load the entire hash table into memory
and perform rendering in-core. When comparing multiple datasets,
we load one hash table per dataset. For significantly larger datasets,
we envision an initial filtering step prior to Barrio, to limit the number of structures being compared. Filtering could be done in our
high-cardinality views (e.g., selecting elements in a scatterplot) or
by querying systems such as ConnectomeExplorer [BAAK∗ 13]. Finally, the number of side-by-side comparison views is limited by
the available screen space. When comparing many neighborhoods,
users should either switch to a higher cardinality comparison mode,or perform filtering prior to looking at juxtaposed views.
8. Case Study: Mitochondria Analysis
We demonstrate the utility of Barrio based on a case study with oneof our collaborating domain experts (also a co-author) who performed a detailed spatial neighborhood analysis of mitochondria.
The visual encodings used in this scenario were developed over thecourse of several months in collaboration with our collaborators,
to create custom views for the comparison of neuronal structures.
After a one-hour training session, the expert operated Barrio independently and also updated the default ADF based on his analysis
interests. The corresponding ADF is included in the supplemental material. Due to the COVID-19 pandemic, the case study was
performed remotely, with our collaborator steering the user interface via the TeamViewer software. The case study consists of theuser-defined analysis scenario ’Mitochondria Analysis’ and examines different aspects of mitochondria neighborhoods. The first two
subtasks ST1 and ST2 are described in Sec. 8.1 and Sec. 8.2. Details on the third subtask are given in the supplemental material.
8.1. Synaptic Neighborhood of Mitochondria (ST1)
Domain Goal: Here, the scientist wanted to analyze the spatial relationships of mitochondria and their surrounding synapses (i.e.,
synapses connecting to the mitochondrion’s cell) to understand therole of mitochondria in synapse formation and strengthening.

Visualizations: Fig. 7 shows the visualizations defined in the ADF
by the domain expert for comparing mitochondria (and their synaptic neighborhod) at low-, medium-, and high-cardinality. For lowcardinality comparisons, our collaborator used juxtaposed synaptic distance trees (Fig 7a), one tree for each mitochondrion underinvestigation. Inspired by phylogenetic trees [SWKP18], the tree
encodes the mitochondrion (root, blue), its surrounding synapses(red), and any mitochondria in cells connected to these synapses(leaves), as well as the distance between structures (edge length).
This view gives a quick insight into the synapse arrangement of theimmediate neighborhood, while abstracting complex 3D morphology. The medium-cardinality comparison uses a grouped barchartand gives a compact view on several mitochondria and their distances to synapses (Fig 7b). The high-cardinality view uses a distance matrix to compare dozens to hundreds of mitochondria simultaneously (Fig 7c). Rows correspond to mitochondria and columns
to synapses. Distances between mitochondria and synapses arecolor-coded, darker colors representing closer distances.
Interactive analysis: The expert started his analysis by using thelist view to select an axonal mitochondrion, as well as a spatiallyclose dendritic mitochondrion. Next, he compared both in the distance tree, looked at their synapses in the 3D neighborhood views,
and found a distant synapse of the dendritic mitochondrion that wasparticularly large. He enabled the EM slice to study the surroundings of this synapse and spotted glycogen granules, cell organelles
responsible for providing energy, in its vicinity. He hypothesizedthat these glycogen granules might be responsible for the genesisof a large synapse otherwise distant from its related mitochondrion(i.e., energy provider). After this initial discovery, our collaborator extended his analysis to four axonal and four dendritic mitochondria, which led Barrio to show the medium-cardinality visualization. Using the grouped bar chart, he could confirm his hypothesis that axonal mitochondria have much closer synapses thandendritic mitochondria. However, he discovered a close synapse toa dendritic mitochondrion in the bar chart (see highlighted bar in
Fig. 7b), which he confirmed to be a shaft synapse by using the3D view. Shaft synapses are not formed at dendritic spines but are
directly located on the cell membrane of the dendrite, and theirfunctionality is not fully understood yet.
8.2. Mitochondria Placement (ST2)
Domain Goal: Here, neuroscientist wanted to investigate how close
mitochondria come to the cell membrane of their neurite and if cellregions with close proximity to mitochondria differ from other areas. Since mitochondria provide energy to the cell, such morphological influences are of biological interest but are difficult to analyze with previous tools that give no quantitative feedback.
Visualizations: For this task, the expert needed to look at the detailed 3D position of a mitochondrion, but also at quantitative visualizations of the distance distribution (Fig. 8), which he defined in
the ADF. Barrio supports color-coding structures in the 3D views,
based on user-defined attributes. For this task, the scientist definedthat each point on a mitochondrion’s surface encoded its distance tothe closest point on the cell membrane. Red regions indicate close
proximity, while blue areas reveal regions more distant to the cell
membrane (Fig. 6c). For low-cardinality comparisons, the expert© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

J. Troidl / Barrio

Figure 8: Analysis views generated for ST2: Mitochondria placement. (a) to (c): low-, medium-, high-cardinality comparison views.
(a) cumulative histogram showing the percentage of a single mitochondrion’s surface that is within a certain distance to the surrounding cell membrane (red means close, blue means far), (b) violin plots for four mitochondria, each showing the distance distribution of the mitochondrion’s surface to the surrounding cell membrane (color and vertical position encode the distance, the width ofthe plot encodes the number of surface points with that distance),(c) scatter plot showing many mitochondria (dots) and their volume
correlated to their distance to the cell membrane.

used small multiples of cumulative histograms for each mitochondrion, encoding the distances from each surface point of the mitochondrion to the cell membrane (Fig. 8a). This view allows users
to judge whether a mitochondrion follows closely the cell membrane, or whether it only comes close in a small area. The mediumcardinality comparison uses juxtaposed violin plots, where eachviolin shape corresponds to a single mitochondrion (Fig. 8b), allowing a more scalable comparison of several structures. The highcardinality comparison is based on a scatterplot where each dot
represents a single mitochondrion (Fig. 8c). The horizontal axis
represents the percentage of a mitochondrion surface that is a userdefined distance (here: 50 nm) or closer to the cell membrane. On
the vertical axis, we encode the mitochondrion’s volume.
Interactive analysis: The expert started his investigation by comparing two axonal and two dendritic mitochondria. First, he studied the immediate proximity of the mitochondria using the EM slice
view (see Fig. 6a). Next, he observed a steeper curve in the cumulative histogram for the axonal mitochonria, indicating that axonal
mitochondria are in general closer to the cell membrane. The scientist used the violin plots to identify mitochondria with a wider
variety in their distance distributions, which all turned out to be
dendritic mitochondria. He studied these mitochondria in the 3Dview to find other neuronal structures close to them. He discoveredsome synapses, which he marked for further investigation. In a laststep, the scientist searched for mitochondria neighborhoods by examining outliers in the scatterplot. While analyzing these outliersin the 3D views, he discovered that boutons often develop synapses
in regions where the mitochondrion is distant from the cell membrane. This pattern has not been observed before, and the domainscientist is now studying this behavior in a follow-up analysis.
Case Study Findings: During this case study, the scientist made new
discoveries in the dataset that he had worked with for months. He
is now performing in-depth follow-up analyses on bouton development close to mitochondria (ST2) and mitochondric spine coverage(ST3). He was also able to adjust the ADF file without prior programming experience, and was comfortable steering and conducting the analysis session in Barrio after a one-hour training session.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

Figure 9: Comparing synapse locations in two different mouse
data sets (purple and green). The 3D views show the four selected
mitochondria neighborhoods. Grouped bars show the distances between mitochondria and their neighboring synapses. Hovering overa bar highlights the corresponding synapse in the 3D neighborhood
view (orange). The young mouse (green, right) exhibits a wider variety of mitochondria-synapse distances than the adult mouse (purple, left), indicating synaptic pruning over time.
9. Qualitative User Study
In addition to the case study, we conducted a qualitative user study
with eight experts to assess the effectiveness and usefulness of Barrio. To demonstrate the ease of sharing analysis scenarios using our
ADF format, and to be able to compare the analysis sessions of different users, we asked each expert to use the ’Mitochondria Analysis’ ADF file as a starting point. We describe the related subtasks in
Sec. 8 and the supplementary material. After their initial analysis,
each expert fine-tuned Barrio to their individual analysis needs. For
example, one scientist loaded two datasets of brain tissue at different ages to analyze the change of synaptic neighborhoods over time(see Fig. 9). Others focused on synapses as target types, or analyzed
synapses that were spatially close to, but unconnected to selected
neurites, which required minor modifications to the ADF.
Participants. We evaluated Barrio with eight experts (P1-P8, 1
male, 7 female) from the Neuroscience Institute at the Universityof Turin and the Center for Brain Science at Harvard University.
All experts (three professors, one postdoctoral researcher, two senior Ph.D. students, and two undergraduates) are experienced inanalyzing 3DEM data of mice brains.
Setup. We met with each participant for a two-hour session remotely over Zoom video conferencing. After a one hour introduction to Barrio and its user interface, we started a data exploration
session. In the study, we used a data set provided by the domain
experts showing layer one of the somatosensory cortex of a fourmonth-old mouse with 1, 088 segmented structures.
Feedback. Fig. 10 shows user ratings for general real-world applicability, tool effectiveness, and relevance for each of the three scenario subtasks. Seven out of eight participants rate questions Q1-Q4
positively, with the remaining participant being neutral. All participants agreed that our tool improved the analysis of 3DEM data.
This is not surprising; other than Barrio, no study participant currently has visual tools available to analyze spatial neighborhoods ofbrain tissue. Prior to Barrio, P2 used Blender for visual explorationand manually computed distances between structures. P2 did not

J. Troidl / Barrio

Limitations. First, some preprocessing steps, such as segmentation, currently still require manual work. For instance, the dendritic
spines and the cell body of all axons and dendrites were manuallylabeled. However, manual data preprocessing makes the analysis oflarger data sets difficult. Second, for specifying ADFs, we offer ahuman-readable format that does not require programming. However, it still requires manual editing and a technical description ofanalysis scenarios. In the future, we could further simplify the ADF
specification for users by offering a visual interface to it.
Figure 10: Barrio user ratings on the three case study subtasks(ST1 - ST3). We show neutral responses (3) in gray, positive responses (4, 5) on the right side in teal and the percentage of participants who responded positively.
have any support for visual comparisons other than taking screenshots. P6, P7, and P8 previously wrote small Python scripts to analyze brain tissue visually.
9.1. Findings
Combining EM data with reconstructed data is essential. Domain experts highly appreciated overlaying the 3D meshes with the
EM image data. It allowed them to verify segmentation accuracy
and enabled them to spot unsegmented properties of brain tissue.
Experts use high-cardinality comparisons to spot trends. Most
participants looked at the high-cardinality visualization first to gain
an overview and to spot outliers or neighborhoods of interest, which
they then examined more closely in the neighborhood view.
Larger data sets could improve neighborhood analysis. Due tothe high resolution of EM data, currently, our data sets do not include full reconstructions of nerve cells, but only small parts ofthem. Neurites and cell organelles can get cut off, which limits thequantitative analysis of those incomplete structures.
Custom analysis scenarios simplified the workflow. By being
able to fine-tune the analysis in the ADF file, scientists were able
to focus on their specific analysis needs and interests. Therefore,
participants were immediately engaged in the tool, as they saw potential benefits, even for their highly individualized scientific goals.
Barrio improves the state of the art. All participants indicatedthat Barrio is useful (four rated it useful, and four rated it very useful) and that the software has a high potential as a visual analysis
tool for 3DEM data. P1, P2, P3, P6, and P7 plan to specify additional analysis scenarios for their data sets.
10. Discussion
Additional features. During the user study, participants suggested
a couple of additional features for Barrio. For instance, it would beinteresting to extend Barrio with visualizations for analyzing the
texture of mitochondria in greater detail. Domain experts P6 andP7 have classified mitochondria based on their texture in EM images into bright, dark, and combined mitochondria, and they are interested in relating these categorizations with our spatial neighborhood analysis. Additionally, P6 and P7 pointed out that knowledge
about spatial neighborhoods of cell organelles such as mitochondria could help to classify cell types in the future.

Expressivity vs. Usability Trade-off. An important lesson learned
were the nuances in the expressivity/usability tradeoff. On the one
hand, our tool is fine-tuned to specific data and the 3DEM workflow (i.e., high in usability), but at the same time, Barrio is flexible enough to reflect the needs of individual researchers (i.e., highin expressivity). This flexibility is crucial to accommodate various
neuroscientists working on 3DEM data. However, it comes withthe additional burden of modifying or defining custom scenarios
and ADFs. To ease this burden, we provide template ADFs, a tutorial, and guidelines on writing custom scenarios [VCG22b]. Furthermore, ADFs are human-readable, allowing scientists to share
their analysis scenarios or start from a provided template.
Flexibility is key. In addition to the flexibility afforded by custom
scenarios, we found that flexibility in terms of data size and thesize of visual comparisons is crucial. Barrio adjusts its views basedon the user-defined scenario and the number of neighborhoods in acomparison. These automatic adjustments based on data and user
goals simplify and streamline a users’ workflow and can be applied
far beyond the domain of neuroscience.
11. Conclusions and Future WorkWe plan to extend Barrio to more usage scenarios involving othercell organelles, such as ribosomes and neuromuscular junctions. So
far, our collaborators are not segmenting and reconstructing thosestructures, which limits their usage in Barrio. We further plan to
use the neighborhood characteristics discovered in Barrio to investigate the extent to which they allow us to automatically label
neuronal structures (e.g., to differentiate excitatory and inhibitory
synapses). We also plan to incorporate topology-centered methods
for the classification of neurites.
We believe that our neighborhood analysis approach is generalizable to other areas, such as single-cell analysis or the exploration ofneighborhoods in city models. Supporting customized analysis scenarios can provide helpful functionality in many domain-specificvisual analysis tools where users have varying analysis goals. Finally, we believe that Barrio can advance neuroscience research,
not just for the presented usage scenario of mitochondria analysis,
but also for future novel domain questions that have yet to be specified by neuroscientists.
12. Acknowledgements
We thank C. Nobre, K. Yin, M. Boido, X. Wang, M. Santos, N.
Marino, M. Lorenzati and S. Stanga for their feedback. This research was supported by the National Science Foundation (NSF)
Award Number IIS-1901030, King Abdullah University of Scienceand Technology (KAUST), and by the University of Torino.
© 2022 The Author(s)Computer Graphics Forum © 2022 The Eurographics Association and John Wiley & Sons Ltd.

J. Troidl / Barrio

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