Multi-Scale Feature Fusion Convolutional Neural Network for Concurrent Segmentation of Left Ventricle and Myocardium in Cardiac MR Images

Author:

Qi Lin1,Zhang Haoran1,Cao Xuehao1,Lyu Xuyang1,Xu Lisheng1,Yang Benqiang2,Ou Yangming3

Affiliation:

1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, 110169, China

2. Department of Radiology, General Hospital of Shenyang Military Region, Shenyang, Liaoning, 110016, China

3. Department of Radiology and Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, 02115, United States

Abstract

Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor’s burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.

Publisher

American Scientific Publishers

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images;International Journal of Online and Biomedical Engineering (iJOE);2023-02-16

2. Anatomical knowledge based level set segmentation of cardiac ventricles from MRI;Magnetic Resonance Imaging;2022-02

3. Nasopharyngeal Organ Segmentation Algorithm Based on Dilated Convolution Feature Pyramid;Lecture Notes in Electrical Engineering;2022

4. FOANet: A Focus of Attention Network with Application to Myocardium Segmentation;2020 25th International Conference on Pattern Recognition (ICPR);2021-01-10

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