$Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data$

Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.

Bibliographic Details
Main Authors:	Khan, Abde Musavvir, Shejuty, Myesha Farid, Talukder, MD. Nafis Shariar, Zubayear, Syed Ibna
Other Authors:	Alam, Md. Golam Rabiul
Format:	Thesis
Language:	en_US
Published:	Brac University 2021
Subjects:	Ejection fraction Deep learning ResUNet 2D Echocardiography Apical 4-Chamber (A4C) Left ventricle CNN U-Net
Online Access:	http://hdl.handle.net/10361/14469

id	10361-14469
record_format	dspace
spelling	10361-144692022-01-26T10:18:23Z Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data Khan, Abde Musavvir Shejuty, Myesha Farid Talukder, MD. Nafis Shariar Zubayear, Syed Ibna Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Ejection fraction Deep learning ResUNet 2D Echocardiography Apical 4-Chamber (A4C) Left ventricle CNN U-Net This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-35). Ejection fraction value denotes how much blood is pumped out of the heart to different parts of the body. It is a routine clinical procedure in heart function assessment, where the left ventricle of the heart has to be manually outlined by doctors in clinical settings to measure the EF value which is time consuming and highly varies by observer. Modern day deep learning methods are able to automatically complete this type of outlining task automatically with much ease and better efficiency even when the model is trained on a deeper neural network and smaller dataset. This paper investigates the deep semantic segmentation networks to find the most accurate one to implement an EF estimation system could be built on the most accurate image segmentation network which will reduce the pressure off the doctors shoulders and stop the eyeball estimation of EF values which is subject to inter-observer variability. This paper evaluated three different image segmentation neural networks namely U-Net, ResUNet, Deep ResUNet to find their accuracy score basing mostly on the dice accuracy metric. The most accurate model of the three Deep ResUNet has been utilized to form Left Ventricle segmentation network for end systole and end diastole images on which volume measurement formula is applied to find out the Ejection Fraction value. Abde Musavvir Khan Myesha Farid Shejuty MD. Nafis Shariar Talukder Syed Ibna Zubayear B. Computer Science 2021-06-02T09:54:46Z 2021-06-02T09:54:46Z 2020 2020-04 Thesis ID: 16301119 ID: 16301123 ID: 16101134 ID: 16301126 http://hdl.handle.net/10361/14469 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Ejection fraction Deep learning ResUNet 2D Echocardiography Apical 4-Chamber (A4C) Left ventricle CNN U-Net
spellingShingle	Ejection fraction Deep learning ResUNet 2D Echocardiography Apical 4-Chamber (A4C) Left ventricle CNN U-Net Khan, Abde Musavvir Shejuty, Myesha Farid Talukder, MD. Nafis Shariar Zubayear, Syed Ibna Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Khan, Abde Musavvir Shejuty, Myesha Farid Talukder, MD. Nafis Shariar Zubayear, Syed Ibna
format	Thesis
author	Khan, Abde Musavvir Shejuty, Myesha Farid Talukder, MD. Nafis Shariar Zubayear, Syed Ibna
author_sort	Khan, Abde Musavvir
title	Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
title_short	Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
title_full	Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
title_fullStr	Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
title_full_unstemmed	Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
title_sort	ejection fraction estimation using deep semantic segmentation neural network on 2d echocardiography data
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14469
work_keys_str_mv	AT khanabdemusavvir ejectionfractionestimationusingdeepsemanticsegmentationneuralnetworkon2dechocardiographydata AT shejutymyeshafarid ejectionfractionestimationusingdeepsemanticsegmentationneuralnetworkon2dechocardiographydata AT talukdermdnafisshariar ejectionfractionestimationusingdeepsemanticsegmentationneuralnetworkon2dechocardiographydata AT zubayearsyedibna ejectionfractionestimationusingdeepsemanticsegmentationneuralnetworkon2dechocardiographydata
_version_	1814308813973487616

Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data

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