Abstract
To identify the heart-related issues the very first step used by clinicians in diagnosis is to correctly identify the clinical indices which are possible by accurate Left Ventricle (LV) segmentation. Our work is related to building a deep learning model that automatically segments the cardiac left ventricle in an echocardiographic image into epicardium, endocardium, and left atrium. We propose the Vgg16 U-Net architecture for LV segmentation in this paper. On the CAMUS dataset, the Vgg16 Unet model is new, and it has demonstrated promising results for endocardium segmentation. The dice metric values achieved for endocardium, epicardium, and left atrium are 0.9412\(\pmb {\pm }\)0.0289, 0.8786\(\pmb {\pm }\)0.0420, and 0.9020\(\pmb {\pm }\)0.0908 respectively.
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Acknowledgment
We would like to acknowledge Sudhish N. George for his comments on the earlier versions of the manuscript.
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Upadhyay, S., Beevi, A.S., Kalady, S. (2023). Left Ventricle Segmentation of 2D Echocardiography Using Deep Learning. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_7
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