Iterative data-driven coronary vessel labeling

Tsai-Pei Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper describes an iterative data-driven algorithm for automatically labeling coronary vessel segments in MDCT images. Such techniques are useful for effective presentation and communication of findings on coronary vessel pathology by physicians and computer-assisted diagnosis systems. The experiments are done on the 18 sets of coronary vessel data in the Rotterdam Coronary Artery Algorithm Evaluation Framework that contain segment labeling by medical experts. The performance of our algorithm show both good accuracy and efficiency compared to previous works on this task.

Original languageEnglish
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
EditorsNaonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781509063413
DOIs
StatePublished - 5 Dec 2017
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: 25 Sep 201728 Sep 2017

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2017-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period25/09/1728/09/17

Keywords

  • Computer-assisted diagnosis
  • Coronary artery imaging
  • MDCT
  • Vessel labeling
  • Vessel tree matching

Fingerprint Dive into the research topics of 'Iterative data-driven coronary vessel labeling'. Together they form a unique fingerprint.

Cite this