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

doi:10.1117/12.2581020

15 February 2021 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

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960B (2021) https://doi.org/10.1117/12.2581020
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

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Steffen Bruns, Jelmer M. Wolterink, Thomas P. W. van den Boogert, José P. Henriques, Jan Baan, R. Nils Planken, and Ivana Išgum "Automatic whole-heart segmentation in 4D TAVI treatment planning CT", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960B (15 February 2021); https://doi.org/10.1117/12.2581020

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