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  <doc>
    <id>8533</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1645</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Active Segmentation Platform for Microscopic Image Classification and Segmentation</title>
    <abstract language="eng">Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.</abstract>
    <parentTitle language="eng">Brain Sciences. 2021</parentTitle>
    <identifier type="doi">https://doi.org/10.3390/brainsci11121645</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-12-07</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Dimiter Prodanov</author>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>9655</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multi-species community platform for comparative neuroscience in teleost fish</title>
    <abstract language="eng">Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community’s research and its easy extensibility will encourage contributions to its continuous expansion.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2024.02.14.580400</identifier>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="PreprintUrn">publish</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Kristian Herrera</author>
    <author>Tinatini Tavhelidse-Suck</author>
    <author>Simon Knoblich</author>
    <author>Ali Seleit</author>
    <author>Jonathan Boulanger-Weill</author>
    <author>Sydney Chambule</author>
    <author>Ariel Aspiras</author>
    <author>Cristina Santoriello</author>
    <author>Owen Randlett</author>
    <author>Joachim Wittbrodt</author>
    <author>Alexander Aulehla</author>
    <author>Jeff W. Lichtman</author>
    <author>Mark Fishman</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <author>Florian Engert</author>
    <author>Yasuko Isoe</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>9967</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish</title>
    <abstract language="eng">Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at the synaptic level remains poorly understood. Dissecting such neural circuits in vertebrates requires precise knowledge of functional neural properties and the ability to directly correlate neural dynamics with the underlying wiring diagram in the same animal. Here we combine functional calcium imaging with ultrastructural circuit reconstruction, using a visual motion accumulation paradigm in larval zebrafish. Using connectomic analyses of functionally identified cells and computational modeling, we show that bilateral inhibition, disinhibition, and recurrent connectivity are prominent motifs for sensory accumulation within the anterior hindbrain. We also demonstrate that similar insights about the structure-function relationship within this circuit can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion of functionally identified neuronal response types. We used our unique ground truth datasets to train and test a novel classifier algorithm, allowing us to assign functional labels to neurons from morphological libraries where functional information is lacking. The resulting feature-rich library of neuronal identities and connectomes enabled us to constrain a biophysically realistic network model of the anterior hindbrain that can reproduce observed neuronal dynamics and make testable predictions for future experiments. Our work exemplifies the power of hypothesis-driven electron microscopy paired with functional recordings to gain mechanistic insights into signal processing and provides a framework for dissecting neural computations across vertebrates.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2025.03.14.643363</identifier>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Jonathan Boulanger-Weill</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Florian Kaempf</author>
    <author>Richard L. Schalek</author>
    <author>Mariela Petkova</author>
    <author>Sumit Kumar Vohra</author>
    <author>Jay H. Savaliya</author>
    <author>Yuelong Wu</author>
    <author>Gregor F. P. Schuhknecht</author>
    <author>Heike Naumann</author>
    <author>Maren Eberle</author>
    <author>Kim N. Kirchberger</author>
    <author>Simone Rencken</author>
    <author>Isaac H. Bianco</author>
    <author>Daniel Baum</author>
    <author>Filippo Del Bene</author>
    <author>Florian Engert</author>
    <author>Jeff W. Lichtman</author>
    <author>Armin Bahl</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>10050</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain</title>
    <abstract language="eng">We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing &gt;180,000 segmented soma, &gt;40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2025.06.10.658982</identifier>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Mariela D. Petkova</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Michał Januszewski</author>
    <author>Tim Blakely</author>
    <author>Kristian J. Herrera</author>
    <author>Gregor F.P. Schuhknecht</author>
    <author>Robert Tiller</author>
    <author>Jinhan Choi</author>
    <author>Richard L. Schalek</author>
    <author>Jonathan Boulanger-Weill</author>
    <author>Adi Peleg</author>
    <author>Yuelong Wu</author>
    <author>Shuohong Wang</author>
    <author>Jakob Troidl</author>
    <author>Sumit Kumar Vohra</author>
    <author>Donglai Wei</author>
    <author>Zudi Lin</author>
    <author>Armin Bahl</author>
    <author>Juan Carlos Tapia</author>
    <author>Nirmala Iyer</author>
    <author>Zachary T. Miller</author>
    <author>Kathryn B. Hebert</author>
    <author>Elisa C. Pavarino</author>
    <author>Milo Taylor</author>
    <author>Zixuan Deng</author>
    <author>Moritz Stingl</author>
    <author>Dana Hockling</author>
    <author>Alina Hebling</author>
    <author>Ruohong C. Wang</author>
    <author>Lauren L. Zhang</author>
    <author>Sam Dvorak</author>
    <author>Zainab Faik</author>
    <author>Kareem I. King, Jr.</author>
    <author>Pallavi Goel</author>
    <author>Julian Wagner-Carena</author>
    <author>David Aley</author>
    <author>Selimzhan Chalyshkan</author>
    <author>Dominick Contreas</author>
    <author>Xiong Li</author>
    <author>Akila V. Muthukumar</author>
    <author>Marina S. Vernaglia</author>
    <author>Teodoro Tapia Carrasco</author>
    <author>Sofia Melnychuck</author>
    <author>TingTing Yan</author>
    <author>Ananya Dalal</author>
    <author>James DiMartino</author>
    <author>Sam Brown</author>
    <author>Nana Safo-Mensa</author>
    <author>Ethan Greenberg</author>
    <author>Michael Cook</author>
    <author>Samantha Finley</author>
    <author>Miriam A. Flynn</author>
    <author>Gary Patrick Hopkins</author>
    <author>Julie Kovalyak</author>
    <author>Meghan Leonard</author>
    <author>Alanna Lohff</author>
    <author>Christopher Ordish</author>
    <author>Ashley L. Scott</author>
    <author>Satoko Takemura</author>
    <author>Claire Smith</author>
    <author>John J. Walsh</author>
    <author>Daniel R. Berger</author>
    <author>Hanspeter Pfister</author>
    <author>Stuart Berg</author>
    <author>Christopher Knecht</author>
    <author>Geoffrey W. Meissner</author>
    <author>Wyatt Korff</author>
    <author>Misha B Ahrens</author>
    <author>Viren Jain</author>
    <author>Jeff W. Lichtman</author>
    <author>Florian Engert</author>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>10058</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish</title>
    <abstract language="eng">Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2025.07.14.664689</identifier>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar vohra</submitter>
    <author>Maren Eberle</author>
    <author>Jonathan Boulanger-Weill</author>
    <author>Mariela D. Petkova</author>
    <author>Gregor F. P. Schuhknecht</author>
    <author>Kristian J. Herrera</author>
    <author>Florian Kämpf</author>
    <author>Virginia M. S. Ruetten</author>
    <author>Jeff W. Lichtman</author>
    <author>Florian Engert</author>
    <author>Owen Randlett</author>
    <author>Armin Bahl</author>
    <author>Yasuko Isoe</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>8991</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3945</pageFirst>
    <pageLast>3958</pageLast>
    <pageNumber/>
    <edition/>
    <issue>7</issue>
    <volume>30</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Visual Interface for Exploring Hypotheses about Neural Circuits</title>
    <abstract language="eng">One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and  morphologies of individual neurons.&#13;
Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. &#13;
The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.</abstract>
    <parentTitle language="eng">IEEE Transactions on Visualization and Computer Graphics</parentTitle>
    <identifier type="doi">10.1109/TVCG.2023.3243668</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2023-02-04</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-89932</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Philipp Harth</author>
    <author>Yasuko Isoe</author>
    <author>Armin Bahl</author>
    <author>Haleh Fotowat</author>
    <author>Florian Engert</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>8993</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Visual Interface for Exploring Hypotheses about Neural Circuits</title>
    <abstract language="eng">One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and  morphologies of individual neurons.&#13;
Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. &#13;
The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-89932</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Philipp Harth</author>
    <author>Yasuko Isoe</author>
    <author>Armin Bahl</author>
    <author>Haleh Fotowat</author>
    <author>Florian Engert</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <series>
      <title>ZIB-Report</title>
      <number>23-07</number>
    </series>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/8993/ZIB-Report-23-7.pdf</file>
  </doc>
  <doc>
    <id>8768</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Vienna, Austria</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Stratification Matrix Viewer for Analysis of Neural Network Data</title>
    <abstract language="eng">The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.</abstract>
    <parentTitle language="eng">Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)</parentTitle>
    <identifier type="doi">10.2312/vcbm.20221194</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-08-31</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Philipp Harth</author>
    <submitter>Philipp Harth</submitter>
    <author>Sumit Vohra</author>
    <author>Daniel Udvary</author>
    <author>Marcel Oberlaender</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
    <collection role="projects" number="PredictingCorticalConnectomes">PredictingCorticalConnectomes</collection>
    <thesisPublisher>Zuse Institute Berlin (ZIB)</thesisPublisher>
  </doc>
  <doc>
    <id>8769</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Active Segmentation: Differential Geometry meets Machine Learning</title>
    <abstract language="eng">Image segmentation is an active area of research for more than 30 years. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem.  This paper presents a project supported in part by the International Neuroinformatics Coordination Facility through the Google Summer of code. The project resulted in an automated image segmentation and classification platform, called Active Segmentation for ImageJ (AS/IJ). The platform integrates a set of filters computing differential geometrical invariants and combines them with machine learning approaches.</abstract>
    <parentTitle language="eng">Proceedings of the 23rd International Conference on Computer Systems and Technologies</parentTitle>
    <identifier type="doi">10.1145/3546118.3546154</identifier>
    <enrichment key="AcceptedDate">2022-06-17</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Dimiter Prodanov</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Sumit Kumar Vohra</author>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
</export-example>
