Cardiac magnetic resonance image segmentation based on convolutional neural network. (December 2020)

• Record Type: Journal Article
• Authors: Liu, Duqiu; Jia, Zheng; Jin, Ming; Liu, Qian; Liao, Zhiliang; Zhong, Junyan; Ye, Haowen; Chen, Gang
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Cardiac magnetic resonance imaging -- Medical image -- Convolutional neural network -- Image saliency -- Image segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105755
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Diagnose cardiac disease by extracting and segmenting MRI of the heart. Cardiac MRI segmentation method based on CNN and image saliency. CNN image segmentation method has better accuracy. Use of CNN is more conducive to observation of cardiac health. Abstract: Objective: In cardiac medical imaging, the extraction and segmentation of the part of interest is the key to the diagnosis of heart disease. Due to irregular diastole and contraction, magnetic resonance imaging (MRI) images have poorly defined boundaries, and traditional segmentation algorithms have poor performance. In this paper, a cardiac MRI segmentation technique using convolutional neural network and image saliency is suggested. Methods: The convolutional neural network is used for detecting target area, filter out the ribs, muscles and the other parts of the anatomy where the contrast is not clearly defined. It can also be used to extract the region of interest (ROI), and compute the contrast of the ROI in order to improve clarity of the heart tissue within the ROI. The cardiac image diagnosis is performed using the obtained saliency image and compared with the segmentation result of the region growth algorithm. Finally, the images of 85 patients were used to train and test the algorithm model. Here, 46 patients were randomly selected for training, and the remaining 39 were harnessed for further tests. Results: Segmentation accuracy of our algorithm model in ventricles, septum and the apex of the heart segment reaches 93.14%, 92.58% and 96.21% respectively, which are better than the segmentation method based on the regional growth technique. Conclusions: The segmentation method using convolutional neural network and image saliency can meet the needs of automatic heart segmentation tasks based on cardiac MRI image sequences. The segmented image is able to assist the doctor to observe the patient's heart health more effectively. As such, our proposed technique has strong potential in clinical applications.