Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI |
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| Affiliation: | 1. Vietnam National University of Agriculture, Vietnam;2. The University of South Australia, Australia;3. Australian Centre for Visual Technologies, The University of Adelaide, Australia;1. College of Electronic Science and Engineering, Jilin University, Changchun, P. R. China;2. Institute of Cardiovascular Science, University College London, London, United Kingdom;3. The Barts Heart Centre, St Bartholomew''s Hospital, Barts Health NHS Trust, London, United Kingdom;4. School of Science, Nanjing University of Science and Technology, Nanjing, P. R. China;5. Institute of Digital Medicine, Third Military Medical University (TMMU), Chongqing, P. R. China;6. School of Electronic Engineering, University of Electronic Science and Technology of China(UESTC), Chengdu, P. R. China;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, PR China;2. Digital Medicine Institute, Biomedical Engineering College, Third Military Medical University, Chongqing, PR China;1. Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia;2. University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia;3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia;4. Australian Research Council Centre of Excellence for Nanoscale Biophotonics, School of Medicine, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia |
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| Abstract: | Left ventricle (LV) segmentation in cardiac MRI is an essential procedure for quantitative diagnosis of various cardiovascular diseases. In this paper, we present a novel fully automatic left ventricle segmentation approach based on convolutional neural networks. The proposed network fully takes advantages of the hierarchical architecture and integrate the multi-scale feature together for segmenting the myocardial region of LV. Moreover, we put forward a dynamic pixel-wise weighting strategy, which can dynamically adjust the weight of each pixel according to the segmentation accuracy of upper layer and force the pixel classifier to take more attention on the misclassified ones. By this way, the LV segmentation performance of our method can be improved a lot especially for the apical and basal slices in cine MR images. The experiments on the CAP database demonstrate that our method achieves a substantial improvement compared with other well-know deep learning methods. Beside these, we discussed two major limitations in convolutional neural networks-based semantic segmentation methods for LV segmentation. |
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