Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Christopher T. Tran; Martin Halicek; James D. Dormer; Animesh Tandon; Tarique Hussain; Baowei Fei

doi:10.1117/12.2549052

28 February 2020 Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net

Christopher T. Tran, Martin Halicek, James D. Dormer, Animesh Tandon, Tarique Hussain, Baowei Fei

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171M (2020) https://doi.org/10.1117/12.2549052
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D-Convolutional Neural Network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95th percentile HD of 5.1 mm, demonstrating good performance in these metrics when compared to literature results. Our preliminary results suggest that our method can be effective in automating RV segmentation.

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Christopher T. Tran, Martin Halicek, James D. Dormer, Animesh Tandon, Tarique Hussain, and Baowei Fei "Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171M (28 February 2020); https://doi.org/10.1117/12.2549052

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KEYWORDS

Image segmentation

Heart

Cardiovascular magnetic resonance imaging

Blood

Convolutional neural networks

Magnetic resonance imaging

Algorithm development

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