Automatic segmentation of cardiac magnetic resonance images based on multi-input fusion network. (September 2021)

• Record Type: Journal Article
• Authors: Shi, Jianshe; Ye, Yuguang; Zhu, Daxin; Su, Lianta; Huang, Yifeng; Huang, Jianlong
• Is Part Of: Computer methods and programs in biomedicine. Volume 209(2021)
• Publication Date: 2021-09
• Subjects: Cardiac magnetic resonance images -- Automatic image segmentation -- MIFNet network -- Multi-scale input -- Fully convolutional network -- DeepLab v1; 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.2021.106323
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Segmentation model based on MIFNet can accurately segment the cardiac anatomy. Parameters of FCN and DeepLab v1 are both more than that of MIFNet. MIFNet is a segmentation CMRI model based on multi-scale input and feature fusion. Dice value of MIFNet segmentation CMRI is 97.238% and the HD is reduced by 16.42%. Our MIFNet gives a loss of 7.45% during the training process, which is half that of our control model. Abstract: Purpose: Using computer-assisted means to process a large amount of heart image data in order to speed up the diagnosis efficiency and accuracy of medical doctors has become a research worthy of investigation. Method: Based on the U-Net model, this paper proposes a multi-input fusion network (MIFNet) model based on multi-scale input and feature fusion, which automatically extracts and fuses features of different input scales to realize the detection of Cardiac Magnetic Resonance Images (CMRI). The MIFNet model is trained and verified on the public data set, and then compared with the segmentation models, namely the Fully Convolutional Network (FCN) and DeepLab v1. Results: MIFNet model segmentation of CMRI significantly improved the segmentation accuracy, and the Dice value reached 97.238%. Compared with FCN and DeepLab v1, the average Hausdorff distance (HD) was reduced by 16.425%. The capacity parameter of FCN is 124.86% of MIFNet, DeepLab v1 is 103.22% of MIFNet. Conclusion: Our proposed MIFNet model reduces the amount of parameters and improves the training speed while ensuring the simultaneous segmentation of overlapping targets. It can help clinicians to more quickly check the patient's CMRI focus area, and thereby improving the efficiency of diagnosis.