Fully automatic segmentation of LV from echocardiography images and calculation of ejection fraction using deep learning

Kulkarni, Pallavi and Madathil, Deepa (2022) Fully automatic segmentation of LV from echocardiography images and calculation of ejection fraction using deep learning. International Journal of Biomedical Engineering and Technology, 40 (3). pp. 241-261. ISSN 1752-6418

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Echocardiography is a widely used ultrasound imaging technique for cardiac health diagnosis. Echocardiography segmentation is a crucial process to evaluate multiple cardiac parameters like ejection fraction, heart wall thicknesses, etc. Recently machine learning techniques especially deep learning using convolution neural network models are finding increasing applications for echo image analysis including its segmentation. In this paper, we have presented a unique convolution neural network (CNN) model for automatic left ventricle (LV) segmentation of echo images. Denoising and feature extraction processes are integrated with the CNN model to enhance its prediction accuracies after training. The proposed system is trained on two-dimensional sequence images of 70 patients and tested on data of 12 patients. An automatic method for evaluation of ejection fraction is appended using the LV segmentation predictions generated by the CNN model. The performance of this CNN architecture is evaluated with recent architectures using various similarities and distance-based majors as well as ejection fraction correlation with ground truth segmentation labelled images. CNN layer visualisation methods are applied to obtain deeper insight into the trained network.

Item Type: Article
Keywords: Echocardiography | Left Ventricle | Convolutional Neural Network | CNN | Autoencoders | Feature Extraction | Layer Visualisation.
Subjects: Physical, Life and Health Sciences > Engineering and Technology
JGU School/Centre: Jindal Institute of Behavioural Sciences
Depositing User: Amees Mohammad
Date Deposited: 01 Dec 2022 06:08
Last Modified: 01 Dec 2022 06:08
Official URL: https://dx.doi.org/10.1504/IJWOE.2022.10050721
URI: https://pure.jgu.edu.in/id/eprint/4904


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