A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology

• DOI: 10.1148/ryai.230132
• Publication Date: 2023-11-15T14:51:35Z
• Authors: Tina Yao; Nicole St. Clair; Gabriel F. Miller; Adam L. Dorfman; Mark A. Fogel; Sunil Ghelani; Rajesh Krishnamurthy; Christopher Z. Lam; Michael Quail; Joshua D. Robinson; David Schidlow; Timothy C. Slesnick; Justin Weigand; Jennifer A. Steeden; Rahul H. Rathod; Vivek Muthurangu
• Keywords: Adult; Heart Ventricles; Heart; Magnetic Resonance Imaging; Univentricular Heart; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Humans; Multicenter Studies as Topic; Child; Retrospective Studies

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 examinations (November 2007-December 2022) 13 institutions training, validation, testing. The contained three DL models: classifier to identify short-axis cine stacks two U-Net 3+ models image cropping segmentation. segmentations were evaluated on the test set (