Confluent Vessel Trees with Accurate Bifurcations

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021(2021)

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摘要
We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow\directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available(1).
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关键词
confluent vessel trees,accurate bifurcations,unsupervised reconstruction,near-capillary vasculature,learning,unsupervised methods,geodesic tubular graphs,symmetric pairwise costs,standard undirected tubular graphs,typical errors,flow directedness,continuous oriented curves,discrete tubular graphs,high-order property,efficient practical algorithm,directed graph enforcing confluence,simple flow-extrapolating arc construction,near-capillary sub-voxel vasculature volumes,reconstruction accuracy
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