@inproceedings{b2c103f2c174480cadd29bbcf627b4c8,
title = "Automatic whole-heart segmentation in 4D TAVI treatment planning CT",
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.",
keywords = "2021 OA procedure",
author = "Steffen Bruns and Wolterink, \{Jelmer M.\} and \{Van Den Boogert\}, \{Thomas P. W.\} and Henriques, \{Jos{\'e} P.\} and Jan Baan and Planken, \{R. Nils\} and Ivana I{\v s}gum and Landman, \{Bennett A.\} and Ivana I{\v s}gum",
note = "Funding Information: This study was funded by the Dutch Technology Foundation (STW, perspectief, P15-26) with participation of Philips Healthcare, Haifa, Israel. Publisher Copyright: {\textcopyright} 2021 SPIE.; SPIE Medical Imaging 2021 : Image Processing ; Conference date: 15-02-2021 Through 20-02-2021",
year = "2021",
month = feb,
day = "15",
doi = "10.1117/12.2581020",
language = "English",
series = "SPIE Conference Proceedings",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, \{Bennett A.\}",
booktitle = "Medical Imaging 2021",
address = "United States",
}