Automatic whole-heart segmentation in 4D TAVI treatment planning CT

Steffen Bruns, Jelmer M. Wolterink, Thomas P. W. Van Den Boogert, José P. Henriques, Jan Baan, R. Nils Planken, Ivana Išgum, Bennett A. Landman, Ivana Išgum

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4D cardiac CT angiography (CCTA) images acquired for transcatheter aortic valve implantation (TAVI) planning provide a wealth of information about the morphology of the heart throughout the cardiac cycle. We propose a deep learning method to automatically segment the cardiac chambers and myocardium in 4D CCTA. We obtain automatic segmentations in 472 patients and use these to automatically identify end-systolic (ES) and end-diastolic (ED) phases, and to determine the left ventricular ejection fraction (LVEF). Our results show that automatic segmentation of cardiac structures through the cardiac cycle is feasible (median Dice similarity coefficient 0.908, median average symmetric surface distance 1.59 mm). Moreover, we demonstrate that these segmentations can be used to accurately identify ES and ED phases (bias [limits of agreement] of 1.81 [-11.0; 14.7]% and -0.02 [-14.1; 14.1]%). Finally, we show that there is correspondence between LVEF values determined from CCTA and echocardiography (-1.71 [-25.0; 21.6]%). Our automatic deep learning approach to segmentation has the potential to routinely extract functional information from 4D CCTA.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE Press
Number of pages9
Publication statusPublished - 15 Feb 2021
EventSPIE Medical Imaging 2021: Image Processing - Online Conference
Duration: 15 Feb 202120 Feb 2021

Publication series

NameSPIE Conference Proceedings


ConferenceSPIE Medical Imaging 2021
CityOnline Conference


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