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Visualisation and Interaction Techniques for the Exploration of the Fruit Fly’S Neural Structure

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Visualisation and Interaction Techniques for the Exploration of the Fruit Fly’S Neural Structure Visualisation and Interaction Techniques for the Exploration of the Fruit Fly’s Neural Structure DIPLOMARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur im Rahmen des Studiums Visual Computing eingereicht von Nicolas Swoboda Matrikelnummer 0425828 an der Fakultät für Informatik der Technischen Universität Wien Betreuung: Ao.Univ.-Prof. Univ.-Doz. Dipl.-Ing. Dr.techn. Eduard Gröller Mitwirkung: Dipl.-Math. Dr. Katja Bühler Wien, 27.02.2020 (Unterschrift Verfasser) (Unterschrift Betreuung) Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at Visualisation and Interaction Techniques for the Exploration of the Fruit Fly’s Neural Structure MASTER’S THESIS submitted in partial fulfillment of the requirements for the degree of Diplom-Ingenieur in Visual Computing by Nicolas Swoboda Registration Number 0425828 to the Faculty of Informatics at the TU Wien Advisor: Ao.Univ.-Prof. Univ.-Doz. Dipl.-Ing. Dr.techn. Eduard Gröller Assistance: Dipl.-Math. Dr. Katja Bühler Vienna, 27.02.2020 (Signature of Author) (Signature of Advisor) Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at Erklärung zur Verfassung der Arbeit Nicolas Swoboda Schloßgasse 2/1/9, 1050 Wien Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die ver- wendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit - einschließlich Tabellen, Karten und Abbildungen -, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall un- ter Angabe der Quelle als Entlehnung kenntlich gemacht habe. (Ort, Datum) (Unterschrift Verfasser) i Acknowledgements This thesis was made possible by the VRVis Zentrum für Virtual Reality und Visual- isierung Forschungs-GmbH. The research company is funded by BMVIT, BMWFW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Cen- ters for Excellent Technologies (854174) which is managed by FFG. My thanks go to Katja Bühler and Meister Eduard Gröller for their helpful advice and patience during the course of my thesis, and for reviewing the work. I want to thank Florian Schulze and everyone else at the VRVis who helped with all my prob- lems. Stefan Bruckner, thank you for your support and thank you especially for waking my interest in the topic. I thank Judith Moosburner who developed the beautiful and innovative design as her Diploma Thesis at the University of the Arts in Zürich. Thank you to all interviewees for helping with the evaluation and the constructive feedback. I thank my family for their unending patience and support. I thank all my friends who encouraged me to continue work on this project when my focus was elsewhere. Finally, an honest thank you to the Republic of Austria for the free access to higher education. iii Abstract In their studies of the brain of the common fruit fly Drosophila melanogaster, neuro- biologists investigate neural connectivity with the goal to discover how complex be- haviour is generated. Gaining information on potential connectivity between neurons is an essential step in their workflow. This thesis presents a way to compute and visualise such potential connectivity information from segmented neurons. It shortens the tedious month-long search for potential connectivity down to a few minutes. Overlaps of arborisations of two or more neurons indicate a potential anatomical connection, and thus a potential functional connection. The computation of this data starts from neuron meshes. The meshes—segmented from confocal light-microscopy images of the fruit fly—are intersected to find overlapping areas, i.e. areas of potential anatomical connectivity. This information can then help to discover actual functional connectivity in a neural circuit. Analysing higher order overlaps, i.e. intersections of more than two arborisations of segmented neuron data in the same location, poses new challenges. The visualisation in 2D sections or 3D is impeded by visual clutter and occlusion. Computation of relevant volumetric information becomes difficult for higher order overlaps, because the number of possible overlaps increases exponentially with the number of arborisations. This makes the pre-computation for all possible combinations infeasible. Previous tools have thus been restricted to the quantification and visualisation of pairwise overlaps. The thesis presents a novel solution addressing these issues for higher order over- laps. A novel abstracting design is coupled with a modern approach for on-demand GPU computations. Our tool calculates for the first time volumetric information of higher order overlaps on the GPU using A-buffers. The thesis addresses the visual com- plexity of the data with the implementation of an innovative novel design created by the graphics designer Judith Moosburner. We realised this design using non-photorealistic rendering techniques and perspicuous user interfaces, including interactive glyphs and linked views on quantitative overlap information. To complement the neuroscientists’ workflows the resulting interactive tool has been integrated into BrainGazer, a software tool for advanced visualisation and exploration of neural images and circuit data. Qual- itative evaluation with neuroscientists and non-expert users demonstrated the utility and usability of the tool. v Kurzfassung Biologen untersuchen neuronale Verbindungen im Gehirn der Fruchtfliege Drosophi- la melanogaster um die Entstehung komplexen Verhaltens zu verstehen. Informationen über die potentielle Konnektivität zwischen Neuronen ist dafür essentiell. Diese Diplom- arbeit beschreibt eine Lösung für die Berechnung und Visualisierung dieser potentiellen Konnektivität von segmentierten Neuronen. Der bisher mehr-monatige Aufwand einer Suche nach potentieller Konnektivität reduziert sich auf einige Minuten. Eine Überlappung zweier oder mehrerer Arborisierungen (Verästelungen von Neu- ronen) impliziert eine potentielle anatomische Verbindung, was wiederum eine tatsäch- liche funktionale Verbindung der Neuronen nahe legt. Berechnet werden diese Schnit- te von Meshes der Neuronen. Die Meshes, segmentiert von Lichtmikroskopiebildern, werden miteinander geschnitten um überlappende Regionen zu finden. Diese Regionen potentieller anatomischer Konnektivität helfen bei der Suche nach funktionaler Konnek- tivität in einem neuronalen Netz. Die Analyse von Schnitten höherer Ordnung (Überlappungen von mehr als zwei Arborisierungen) stellt uns vor neue Aufgaben. Die Visualisierung mehrerer Meshes in 2D oder 3D erzeugt Verdeckungen. Die Berechnung der volumetrischen Informationen wird schwierig, denn die Anzahl der möglichen Schnitte steigt exponentiell mit der An- zahl der Meshes. Eine Vorberechnung ist daher nicht umsetzbar. Bisherige Werkzeuge beschränken die Quantifizierung und Visualisierung auf paarweise Schnitte. Die Diplomarbeit präsentiert eine Implementierung, die sich dieser Probleme wid- met. Sie verbindet ein neuartiges abstrahierendes Design mit einem modernen Ansatz für on-demand GPU Berechnungen. Zum ersten Mal werden A-Buffer auf der GPU für volumetrische Berechnungen genutzt. Das neue innovative Design von Grafikdesignerin Judith Moosburner behandelt die komplexen visuellen Ansprüche der neuronalen Daten. Mittels nicht-fotorealistischer Rendering Techniken, interaktiver Glyphen in 2D und 3D, verständlicher Benutzeroberflächen und verlinkter Darstellungen der quantitativen Daten haben wir das Design verwirklicht. Diese Implementierung wurde in BrainGazer integriert, um die Arbeitsschritte der Neurowissenschafter zu unterstützen. Eine qualita- tive Evaluierung mit Neurobiologen und Nichtfachleuten belegt den Nutzen des neuen Werkzeugs und zeigt, dass es intuitiv verwendbar ist. vii Contents 1 Introduction1 1.1 Problem Statement . .1 1.2 Requirements . .2 1.3 Thesis Overview . .2 2 Introduction to Neural Circuitry3 2.1 Circuit Neuroscience . .3 2.2 Drosophila Melanogaster . .4 2.3 Data Acquisition and Enhancement . 10 2.4 Scientific Questions . 12 3 Related Work 15 3.1 Rendering Techniques . 15 3.2 Visualisation of Neural Connectivity . 16 3.3 Occlusion and Intersection in Visualisation . 22 3.4 Discussion . 22 4 Information and Interaction Design 23 4.1 Background . 23 4.2 Creating the new Design . 26 4.3 Object, Shape, and Colour Design . 32 4.4 Connectivity and Interaction Design . 35 5 Implementation 39 5.1 Existing Environment: BrainGazer . 39 5.2 Computational Pipeline . 42 5.3 Basic Data Structure: A-buffer . 45 6 Volume Estimation and Rendering 49 6.1 Volume Estimation . 49 6.2 Quantitative Information . 61 ix 6.3 Rendering Techniques . 63 7 Interaction 73 7.1 Exploration in 3D . 73 7.2 Glyph Interaction . 74 7.3 Selection of Neuronal Structures . 79 7.4 Filtering . 80 8 Evaluation 83 8.1 Evaluation Setup . 83 8.2 Quantitative Evaluation . 84 8.3 Qualitative Evaluation . 84 8.4 User Feedback . 84 8.5 Discussion . 86 9 Conclusion and Future Work 89 9.1 Conclusion . 89 9.2 Future Work . 90 Bibliography 91 x CHAPTER 1 Introduction The fruit fly Drosophila melanogaster has long been a popular model organism in the neuroscience community. More specifically in the field of circuit neuroscience it is studied to link complex behaviour with neuronal circuits in the brain. In cooperation with our partners at the Institute of Molecular Pathology (IMP) in Vienna we develop tools to improve the scientists’ workflows or create novel views on the fly’s data. The scientists acquire this data via confocal microscopy to create 3D images of
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