Data-driven feature learning for myocardial segmentation of CP-BOLD MRI

Anirban Mukhopadhyay, Ilkay Oksuz, Marco Bevilacqua, Rohan Dharmakumar, Sotirios A. Tsaftaris

Research output: Contribution to journalConference paperpeer-review


Multi–slice computed tomography image series are a valuable source of information to extract shape and motion parameters of the heart. We present a method how to segment and label all main chambers (both ventricles and atria) and connected vessels (arteries and main vein trunks) from such images and to track their movement over the cardiac cycle. A framework is presented to construct a multi–surface triangular model enclosing all blood–filled cavities and the main myocardium as well as to adapt this model to unseen images, and to propagate it from phase to phase. While model construction still requires a reasonable amount of user interaction, adaptation is mostly automated, and propagation works fully automatically. The adaptation method by deformable surface models requires a set of landmarks to be manually located for one of the cardiac phases for model initialisation.
Original languageEnglish
Pages (from-to)189-197
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Early online date21 Jun 2015
Publication statusPublished - 2015


  • Dictionary learning
  • Segmentation


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