A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation

The segmentation of the left ventricle (LV) wall in four-chamber view cardiac sequential image is significant for cardiac disease diagnosis and cardiac mechanisms study; however, there is no successful reported work on sequential four-chambered view LV wall segmentation due to the complex four-chamb...

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Autores principales:	Yu Wang, Wanjun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	four-chamber view cardiac recurrent neural network image segmentation left ventricle wall cardiac state estimation Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/e2b3cc1771434b8c9aa3ee3f48b34164
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spelling	oai:doaj.org-article:e2b3cc1771434b8c9aa3ee3f48b341642021-11-30T14:14:07ZA Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation2296-418510.3389/fbioe.2021.696227https://doaj.org/article/e2b3cc1771434b8c9aa3ee3f48b341642021-08-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbioe.2021.696227/fullhttps://doaj.org/toc/2296-4185The segmentation of the left ventricle (LV) wall in four-chamber view cardiac sequential image is significant for cardiac disease diagnosis and cardiac mechanisms study; however, there is no successful reported work on sequential four-chambered view LV wall segmentation due to the complex four-chamber structure and diversity of wall motion. In this article, we propose a dense recurrent neural network (RNN) algorithm to achieve accurately LV wall segmentation in a four-chamber view MRI time sequence. In the cardiac sequential LV wall process, not only the sequential accuracy but also the accuracy of each image matters. Thus, we propose a dense RNN to provide compensation for the first long short-term memory (LSTM) cells. Two RNNs are combined in this work, the first one aims at providing information for the first image, and the second RNN generates segmentation result. In this way, the proposed dense RNN improves the accuracy of the first frame image. What is more is that, it improves the effectiveness of information flow between LSTM cells. Obtaining more competent information from the former cell, frame-wise segmentation accuracy is greatly improved. Based on the segmentation result, an algorithm is proposed to estimate cardiac state. This is the first time that deals with both cardiac time-sequential LV segmentation problems and, robustly, estimates cardiac state. Rather than segmenting each frame separately, utilizing cardiac sequence information is more stable. The proposed method ensures an Intersection over Union (IoU) of 92.13%, which outperforms other classical deep learning algorithms.Yu WangWanjun ZhangFrontiers Media S.A.articlefour-chamber view cardiacrecurrent neural networkimage segmentationleft ventricle wallcardiac state estimationBiotechnologyTP248.13-248.65ENFrontiers in Bioengineering and Biotechnology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	four-chamber view cardiac recurrent neural network image segmentation left ventricle wall cardiac state estimation Biotechnology TP248.13-248.65
spellingShingle	four-chamber view cardiac recurrent neural network image segmentation left ventricle wall cardiac state estimation Biotechnology TP248.13-248.65 Yu Wang Wanjun Zhang A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
description	The segmentation of the left ventricle (LV) wall in four-chamber view cardiac sequential image is significant for cardiac disease diagnosis and cardiac mechanisms study; however, there is no successful reported work on sequential four-chambered view LV wall segmentation due to the complex four-chamber structure and diversity of wall motion. In this article, we propose a dense recurrent neural network (RNN) algorithm to achieve accurately LV wall segmentation in a four-chamber view MRI time sequence. In the cardiac sequential LV wall process, not only the sequential accuracy but also the accuracy of each image matters. Thus, we propose a dense RNN to provide compensation for the first long short-term memory (LSTM) cells. Two RNNs are combined in this work, the first one aims at providing information for the first image, and the second RNN generates segmentation result. In this way, the proposed dense RNN improves the accuracy of the first frame image. What is more is that, it improves the effectiveness of information flow between LSTM cells. Obtaining more competent information from the former cell, frame-wise segmentation accuracy is greatly improved. Based on the segmentation result, an algorithm is proposed to estimate cardiac state. This is the first time that deals with both cardiac time-sequential LV segmentation problems and, robustly, estimates cardiac state. Rather than segmenting each frame separately, utilizing cardiac sequence information is more stable. The proposed method ensures an Intersection over Union (IoU) of 92.13%, which outperforms other classical deep learning algorithms.
format	article
author	Yu Wang Wanjun Zhang
author_facet	Yu Wang Wanjun Zhang
author_sort	Yu Wang
title	A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
title_short	A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
title_full	A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
title_fullStr	A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
title_full_unstemmed	A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation
title_sort	dense rnn for sequential four-chamber view left ventricle wall segmentation and cardiac state estimation
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e2b3cc1771434b8c9aa3ee3f48b34164
work_keys_str_mv	AT yuwang adensernnforsequentialfourchamberviewleftventriclewallsegmentationandcardiacstateestimation AT wanjunzhang adensernnforsequentialfourchamberviewleftventriclewallsegmentationandcardiacstateestimation AT yuwang densernnforsequentialfourchamberviewleftventriclewallsegmentationandcardiacstateestimation AT wanjunzhang densernnforsequentialfourchamberviewleftventriclewallsegmentationandcardiacstateestimation
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A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation

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