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

• Published in: Frontiers in bioengineering and biotechnology Vol. 9; p. 696227
• Main Authors: Wang, Yu, Zhang, Wanjun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 06.08.2021
• Subjects: Bioengineering and Biotechnology; cardiac state estimation; four-chamber view cardiac; image segmentation; left ventricle wall; recurrent neural network; recurrent neural network; cardiac state estimation; image segmentation; left ventricle wall; four-chamber view cardiac
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2021.696227

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.