TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

Deepak Keshwani, Yoshiro Kitamura, Satoshi Ihara, Satoshi Iizuka, Edgar Simo-Serra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentation problem. However, vessels such as hepatic and portal vein look very similar locally and need to be traced to their source for robust label assignment. Therefore, semantic segmentation by looking at local 3D patch results in noisy misclassifications. To tackle this, we propose a novel multi-task deep learning architecture for vessel tree reconstruction. The network architecture simultaneously solves the task of detecting voxels on vascular centerlines (i.e. nodes) and estimates connectivity between center-voxels (edges) in the tree structure to be reconstructed. Further, we propose a novel connectivity metric which considers both inter-class distance and intra-class topological distance between center-voxel pairs. Vascular trees are reconstructed starting from the vessel source using the learned connectivity metric using the shortest path tree algorithm. A thorough evaluation on public IRCAD dataset shows that the proposed method considerably outperforms existing semantic segmentation based methods. To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages14-23
Number of pages10
ISBN (Print)9783030597245
DOIs
Publication statusPublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12266 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/8

Keywords

  • Centerline detection
  • Computed tomography
  • Deep learning
  • Liver vessel
  • Vessel segmentation
  • Vessel tree reconstruction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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