Whole Heart Anatomical Refinement from CCTA Using Extrapolation and Parcellation

Hao Xu*, Steven A. Niederer, Steven E. Williams, David E. Newby, Michelle C. Williams, Alistair A. Young

*Corresponding author for this work

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

4 Citations (Scopus)


Coronary computed tomography angiography (CCTA) provides detailed anatomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcellation, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com.

Original languageEnglish
Title of host publicationFunctional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
EditorsDaniel B. Ennis, Luigi E. Perotti, Vicky Y. Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030787097
Publication statusPublished - 2021
Event11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 - Virtual, Online
Duration: 21 Jun 202125 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12738 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
CityVirtual, Online


  • CCTA
  • U-Net
  • Whole heart segmentation


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