Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients

Julia Kar; Michael V Cohen; Samuel A McQuiston; Teja Poorsala; Christopher M Malozzi

doi:10.1117/1.JMI.11.2.024003

Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients

J Med Imaging (Bellingham). 2024 Mar;11(2):024003. doi: 10.1117/1.JMI.11.2.024003. Epub 2024 Mar 19.

Authors

Julia Kar¹, Michael V Cohen², Samuel A McQuiston³, Teja Poorsala⁴, Christopher M Malozzi²

Affiliations

¹ University of South Alabama, Departments of Mechanical Engineering and Pharmacology, Alabama, United States.
² University of South Alabama, Department of Cardiology, College of Medicine, Alabama, United States.
³ University of South Alabama, Department of Radiology, Alabama, United States.
⁴ University of South Alabama, Departments of Oncology and Hematology, Alabama, United States.

PMID: 38510543
PMCID: PMC10950093 (available on 2025-03-19)
DOI: 10.1117/1.JMI.11.2.024003

Abstract

Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach's alpha ( $C - α$ ) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations. Results: Myocardial classification results against ground-truth were $Dice = 0.89$ , $APD = 2.4 mm$ , and $accuracy = 97 %$ for the validation set and $Dice = 0.90$ , $APD = 2.5 mm$ , and $accuracy = 97 %$ for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were $CI = - 1.36 %$ to 2.42%, p-value < 0.001 for LVEF ( $58 \pm 5 %$ versus $57 \pm 6 %$ ), and $CI = - 0.71 %$ to 0.63%, p-value < 0.001 for longitudinal strain ( $- 15 \pm 2 %$ versus $- 15 \pm 3 %$ ). Conclusions: The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.

Keywords: CTRCD; artificial intelligence; cardiotoxicity; chamber quantification; deep-learning; displacement encoding with stimulated echoes.