Whole Brain Vessel Graphs: a Dataset and Benchmark for Graph Learning and Neuroscience (Vesselgraph)

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph) Johannes C. Paetzold†‡, Julian McGinnis†, Suprosanna Shit†, Ivan Ezhov†, Paul Büschl†, Chinmay Prabhakar*, Mihail I. Todorov‡, Anjany Sekuboyina*, Georgios Kaissis†, Ali Ertürk‡, Stephan Günnemann†, Bjoern H. Menze* †Technical University of Munich, ‡Helmholtz Zentrum München, *University of Zürich [email protected] Abstract 1 Biological neural networks define the brain function and intelligence of humans 2 and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal 3 organization is closely interconnected with the spatial organization of the brain’s 4 microvasculature, which supplies oxygen to the neurons and builds a complemen- 5 tary spatial graph. This vasculature (or the vessel structure) plays an important 6 role in neuroscience; for example, the organization of (and changes to) vessel 7 structure can represent early signs of various pathologies, e.g. Alzheimer’s disease 8 or stroke. Recently, advances in tissue clearing have enabled whole brain imaging 9 and segmentation of the entirety of the mouse brain’s vasculature. Building on 10 these advances in imaging, we are presenting an extendable dataset of whole-brain 11 vessel graphs based on specific imaging protocols. Specifically, we extract vascular 12 graphs using a refined graph extraction scheme leveraging the volume rendering 13 engine Voreen and provide them in an accessible and adaptable form through the 14 OGB and PyTorch Geometric dataloaders. Moreover, we benchmark numerous 15 state-of-the-art graph learning algorithms on the biologically relevant tasks of 16 vessel prediction and vessel classification using the introduced vessel graph dataset. 17 Our work paves a path towards advancing graph learning research into the field 18 of neuroscience. Complementarily, the presented dataset raises challenging graph 19 learning research questions for the machine learning community, in terms of incor- 20 porating biological priors into learning algorithms, or in scaling these algorithms 1 21 to handle sparse,spatial graphs with millions of nodes and edges. 22 1 Introduction 23 Human intelligence and brain function are defined by the cerebral biological neuronal network, the 24 so-called connectome. The entirety of all single neurons forms an ultra-large, spatial, hierarchical 25 and structured graph. Imaging and reconstructing these whole-brain graphs on a single-neuron 26 level is one of the key problems in neuroscience. Neuronal organization is closely linked to the 27 vascular network, as vessels supply the neurons with nutrients (e.g. oxygen). Specifically, the vessel 28 topology determines the maximum metabolic load and determines neural growth patterns [1]. Vascular 29 organisation, particularly in regards to vessel sizes and numbers of capillary links, varies substantially 30 between brain regions, see Supplementary Figure 5 and 6. Moreover, its organization and changes to 31 its structure are early signs for the development of specific diseases, e.g. Alzheimer’s disease [2, 3] 1All datasets and code are available for download at https://github.com/jocpae/VesselGraph. Submitted to the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks. Do not distribute. Whole Brain Images of various modalities Open Source Hosting of Datasets and Code SEAL GCN’s MLP Node2Vec … Segmentation and Graph Extracted whole brain Integration to Graph Baseline Algorithms Extraction Pipeline vessel graphs Data/Learning Platforms Figure 1: Graphical Abstract of VesselGraph. 32 or even COVID-19 encephalopathy [4]. As an initial step towards understanding the neuronal and 33 vascular connectome (also known as the angiome [5]), reliable imaging and segmentation methods 34 are required. To this day, whole-brain imaging and segmentation of all neurons in the brain remains 35 elusive. On the other hand, advances in tissue clearing and deep learning have enabled imaging and 36 segmentation of the whole murine brain vasculature down to the microcapillary level [6, 7]. 37 Nonetheless, a binary segmentation of the vasculature is insufficient for a full, abstract description of 38 the vascular connectome. To enable a comprehensive hierarchical description of the spatial vessel 39 structure and anatomy, a graph representation of the brain with detailed features is required. This 40 work provides the first large-scale, reproducible graph dataset thereof. 41 We believe that such a graph representation can facilitate research and understanding in many fields. 42 The correction of imperfect vascular imaging and segmentation based on such an enhanced vascular 43 graph, could one day enable the simulation of blood-flow (hemodynamic modeling), the study of 44 vessel anatomy, connectivity, collateralization/anastomosis and structural abnormalities. Future 45 studies using enhanced datasets could find our approach useful to study pathologies associated with 46 neurovascular disorders, such as stroke and dementia, given that obstacles such as plaques would be 47 accounted for. 48 Evidently, the study of such spatial graphs with millions of nodes requires its own set of methods; we 49 believe that the recent rise of advanced machine learning methods for graphs will provide suitable 50 approaches to efficiently and accurately permit drawing deep insight from vascular graphs. This, in 51 turn, will foster the development of methods capable of dealing with massive, but sparsely connected 52 circular graphs, for inference on these graphs, and inference under structural and functional prior 53 constraints that are present in such spatial physical 3D networks. 54 In this work we benchmark two exemplary and biologically relevant tasks using both traditional 55 approaches and advanced graph learning. First, in order to improve the structure and anatomical 56 fidelity of the extracted graphs, we benchmark vessel (link) prediction. As a second task, we 57 benchmark vessel (node) classification into the three main classes (capillaries, arterioles/venules, and 58 arteries/veins), which represent biologically meaningful classifications by vessel size, and whose 59 relevance for hemodynamics has been demonstrated in stroke and oxygenation modeling [8]. 60 1.1 Whole brain vascular imaging and segmentation 61 Novel imaging methods, e.g. tissue-clearing-based methods [9–12], VesSAP [6], Tubemap [7] and 62 the work by diGiovanna et al. [13] have enabled the imaging of the full vascular structure on a 63 whole-brain scale [1]. 64 The segmentation of the resulting ultra-large and unbalanced images with thousands of pixels in 65 each dimension (e.g. 3096 × 4719 × 1867 pixels [6]) is a challenging computer vision task which is 66 strongly affected by technical imaging imperfections. The best-performing segmentation approaches 67 rely on deep learning, e.g. using the U-Net architecture, and are only trained on selected, manually 68 annotated sub-volumes of the whole brain images [6, 7, 1], leading to further imperfections in the 69 segmentation masks. 2 Figure 2: Left: 3D imaging of the whole mouse brain vasculature [6] and right; the corresponding rendering of our whole brain spatial vessel graphs; the edges (vessels) are rendered with the average radius feature. 70 The process presented in our work commences with segmentations of whole-brain vessel images, for 71 which we use publicly available data from lightsheet microscopy (VesSAP), two-photon microscopy 72 and a synthetic blood vessel dataset. For details refer to Appendix B.4. In the future, we will 73 continuously increase the dataset with whole-brain images and segmentation as they become publicly 74 available. 75 1.2 Graph learning 76 Machine learning on graphs is a highly relevant research field which aims to develop efficient machine 77 learning algorithms exploiting the unique properties of graphs, such as structure neighborhoods and 78 the sparse representation of complex systems. Our work concerns a particularly challenging domain 79 - spatial, structured and ultra large biological graphs. In this paper we utilize and benchmark two 80 fundamental graph learning tasks: node classification and link prediction to study the biological 81 properties of the vascular connectome. 82 A widely recognized concept for node classification is the adaption of deep learning techniques to 83 graphs via graph convolutional networks (GCN) [14], a concept which was adapted and extended 84 for many of the algorithms that we implemented, such as such as GNNs, GCNs, and GAEs [15–23]. 85 A key approach for link prediction is a so-called labeling trick [24], which is a concept to come up 86 with sensible training data. The SEAL labeling trick used in our work constructs a subgraph for two 87 candidate nodes (enclosing subgraph) and aims to learn a functional mapping on the subgraph to 88 predict link existence [25]. 89 1.3 Our contribution 90 Our main contributions are: 91 1. We extract a set of standardized whole-brain vessel graphs based on whole, segmented 92 murine brain images. 93 2. We publicly release said dataset in an easily accessible and adaptable format for use in 94 graph learning benchmarking by implementing the open graph benchmark (OGB) [26] and 95 PyTorch Geometric data loaders [27]. 96 3. In addition to our standard vessel graph, in which bifurcation points are nodes and vessels 97 are edges, we propose an alternative representation of the vascular connectome as a line 3 98 graph (where vessels become nodes),

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