Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation

Zhuowei Li, Qing Xia, Wenji Wang, Zhennan Yan, Ruohan Yin, Changjie Pan, Dimitris Metaxas

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

1 Scopus citations

Abstract

Automatic and accurate coronary artery labeling technique from CCTA can greatly reduce clinician’s manual efforts and benefit large-scale data analysis. Current line of research falls into two general categories: knowledge-based methods and learning-based techniques. However, no matter in which fashion it is developed, the formation of problem finally attributes to tree-structured centerline classification and requires hand-crafted features. Here, instead we present a new concise, effective and flexible framework for automatic coronary artery labeling by modeling the task as coronary artery parsing task. An intact pipeline is proposed and two paralleled sub-modules are further designed to consume volumetric image and unordered point cloud correspondingly. Finally, a self-contained loss is proposed to supervise labeling process. At experiment section, we conduct comprehensive experiments on collected 526 CCTA scans and exhibit stable and promising results.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages130-138
Number of pages9
ISBN (Print)9783030598600
DOIs
StatePublished - 2020
Event11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/4/2010/4/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Coronary artery labeling
  • Deep learning
  • Parsing
  • Point cloud

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