Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

• URL: http://arxiv.org/abs/2108.13233v1
• Date: Mon, 30 Aug 2021 13:40:48 GMT
• Title: Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
• Authors: Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul B\"uschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ert\"urk, Stephan G\"unnemann, Bjoern H. Menze
• Abstract summary: We present an extendable dataset of whole-brain vessel graphs based on specific imaging protocols. We benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification. Our work paves a path towards advancing graph learning research into the field of neuroscience.
• Score: 3.846749674808336
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is closely interconnected with the spatial organization of the brain's microvasculature, which supplies oxygen to the neurons and builds a complementary spatial graph. This vasculature (or the vessel structure) plays an important role in neuroscience; for example, the organization of (and changes to) vessel structure can represent early signs of various pathologies, e.g. Alzheimer's disease or stroke. Recently, advances in tissue clearing have enabled whole brain imaging and segmentation of the entirety of the mouse brain's vasculature. Building on these advances in imaging, we are presenting an extendable dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, we extract vascular graphs using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provide them in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders. Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset. Our work paves a path towards advancing graph learning research into the field of neuroscience. Complementarily, the presented dataset raises challenging graph learning research questions for the machine learning community, in terms of incorporating biological priors into learning algorithms, or in scaling these algorithms to handle sparse,spatial graphs with millions of nodes and edges. All datasets and code are available for download at https://github.com/jocpae/VesselGraph .

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