Virtual reality framework for editing and exploring medial axis representations of nanometric scale neural structures

Abstract
We present a novel virtual reality (VR) based framework for the exploratory analysis of nanoscale 3D reconstructions of cellular structures acquired from rodent brain samples through serial electron microscopy. The system is specifically targeted on medial axis representations (skeletons) of branched and tubular structures of cellular shapes, and it is designed for providing to domain scientists: i) effective and fast semi-automatic interfaces for tracing skeletons directly on surface-based representations of cells and structures, ii) fast tools for proofreading, i.e., correcting and editing of semi-automatically constructed skeleton representations, and iii) natural methods for interactive exploration, i.e., measuring, comparing, and analyzing geometric features related to cellular structures based on medial axis representations. Neuroscientists currently use the system for performing morphology studies on sparse reconstructions of glial cells and neurons extracted from a sample of the somatosensory cortex of a juvenile rat. The framework runs in a standard PC and has been tested on two different display and interaction setups: PC-tethered stereoscopic head-mounted display (HMD) with 3D controllers and tracking sensors, and a large display wall with a standard gamepad controller. We report on a user study that we carried out for analyzing user performance on different tasks using these two setups.

Citation
Boges, D., Agus, M., Sicat, R., Magistretti, P. J., Hadwiger, M., & Calì, C. (2020). Virtual reality framework for editing and exploring medial axis representations of nanometric scale neural structures. Computers & Graphics, 91, 12–24. doi:10.1016/j.cag.2020.05.024

Acknowledgements
This work is supported by KAUST King Abdullah University of Science and Technology KAUST-EPFL Alliance for Integrative Modeling of Brain Energy Metabolism https://www.kaust.edu.sa/en under KAUST CRG6 Grant No. 2313. We thank all the participants of the user study from KAUST and from the Neuroscience Institute ”Cavalieri Ottolenghi”. We also thank the anonymous reviewers for useful comments and suggestions.

Publisher
Elsevier BV

Journal
Computers & Graphics

DOI
10.1016/j.cag.2020.05.024

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0097849320300789

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