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A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac Magnetic Resonance Image Registry of Single Ventricle Patients

Yao, Tina; St. Clair, Nicole; Miller, Gabriel F; Dorfman, Adam L; Fogel, Mark A; Ghelani, Sunil; Krishnamurthy, Rajesh; ... Muthurangu, Vivek; + view all (2023) A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac Magnetic Resonance Image Registry of Single Ventricle Patients. Radiology: Artificial Intelligence 10.1148/ryai.230132. (In press). Green open access

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Abstract

Purpose: To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (FORCE). / Materials and Methods: This retrospective study used 250 cardiac MRI examinations (November 2007–December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two UNet 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. / Results: There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (Bias: -0.6 mL/m2, LOA: -20.6–19.5 mL/m2), and end-systolic volume (ESV) (Bias: - 1.1 mL/m2, LOA: -18.1–15.9 mL/m2), with high intraclass correlation coefficients (ICC > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (Bias: -1.9 g/m2, LOA: -17.3–13.5 g/m2) and a ICC (0.94). There was also acceptable agreement for stroke volume (Bias:0.6 mL/m2, LOA: -17.2–18.3 mL/m2) and ejection fraction (Bias:0.6%, LOA: -12.2%–13.4%), with high ICCs (> 0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. / Conclusion: The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry.

Type: Article
Title: A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac Magnetic Resonance Image Registry of Single Ventricle Patients
Open access status: An open access version is available from UCL Discovery
DOI: 10.1148/ryai.230132
Publisher version: https://doi.org/10.1148/ryai.230132
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease
URI: https://discovery.ucl.ac.uk/id/eprint/10181939
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