Version 1
: Received: 23 August 2023 / Approved: 24 August 2023 / Online: 24 August 2023 (07:20:53 CEST)
How to cite:
Bekkouche, A.; MERZOUG, M.; HADJILA, F.; DAOUD, O. Segmentation of the Left Ventricle Using Improved UNET Neural Networks. Preprints2023, 2023081719. https://doi.org/10.20944/preprints202308.1719.v1
Bekkouche, A.; MERZOUG, M.; HADJILA, F.; DAOUD, O. Segmentation of the Left Ventricle Using Improved UNET Neural Networks. Preprints 2023, 2023081719. https://doi.org/10.20944/preprints202308.1719.v1
Bekkouche, A.; MERZOUG, M.; HADJILA, F.; DAOUD, O. Segmentation of the Left Ventricle Using Improved UNET Neural Networks. Preprints2023, 2023081719. https://doi.org/10.20944/preprints202308.1719.v1
APA Style
Bekkouche, A., MERZOUG, M., HADJILA, F., & DAOUD, O. (2023). Segmentation of the Left Ventricle Using Improved UNET Neural Networks. Preprints. https://doi.org/10.20944/preprints202308.1719.v1
Chicago/Turabian Style
Bekkouche, A., Fethallah HADJILA and Omar DAOUD. 2023 "Segmentation of the Left Ventricle Using Improved UNET Neural Networks" Preprints. https://doi.org/10.20944/preprints202308.1719.v1
Abstract
The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.
Keywords
left ventricle segmentation; fully convolutional networks; U-NET; inception modules; medical image segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
