Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture

Roshan Reddy Upendra; Shusil Dangi; Cristian A. Linte

doi:10.1117/12.2550656

16 March 2020 Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture

Roshan Reddy Upendra, Shusil Dangi, Cristian A. Linte

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113152Y (2020) https://doi.org/10.1117/12.2550656
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Cine cardiac magnetic resonance imaging (CMRI), the current gold standard for cardiac function analysis, provides images with high spatio-temporal resolution. Computing clinical cardiac parameters like ventricular blood-pool volumes, ejection fraction and myocardial mass from these high resolution images is an important step in cardiac disease diagnosis, therapy planning and monitoring cardiac health. An accurate segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool is crucial for computing these clinical cardiac parameters. U-Net inspired models are the current state-of-the-art for medical image segmentation. SegAN, a novel adversarial network architecture with multi-scale loss function, has shown superior segmentation performance over U-Net models with single-scale loss function. In this paper, we compare the performance of stand-alone U-Net models and U-Net models in SegAN framework for segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool from the 2017 ACDC segmentation challenge dataset. The mean Dice scores achieved by training U-Net models was on the order of 89.03%, 89.32% and 88.71% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively. The mean Dice scores achieved by training the U-Net models in SegAN framework are 91.31%, 88.68% and 90.93% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively.

Citation Download Citation

Roshan Reddy Upendra, Shusil Dangi, and Cristian A. Linte "Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152Y (16 March 2020); https://doi.org/10.1117/12.2550656

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