Semantic Segmentation of Subcortical Brain Structures Based on DenseMedic Network
Yang Binbin, Liu Linwen, Zhang Weiwei*
(State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China)
Abstract：Subcortical segmentation is the basis for computer-aided diagnosis and treatment of central nervous system diseases. By segmenting and analyzing the brain structures in MRI image, early diagnosis and treatment of diseases such as autism spectrum disorder, stroke, and brain tumors can be performed. In order to solve the problem of accurate subcortical segmentation, based on the basic theory of deep learning, an algorithm named DenseMedic for subcortical segmentation on MRI image is proposed. First, the OreoDown method increases the growth rate of the characteristic receptive field by increasing the stride of convolutions in early layers, and uses convolutions with constant input and output sizes to restore the network depth in a sandwich-like manner, so that the increase in growth rate brings an effective receptive field increase. Second, DenseMedic uses the idea of DenseNet to instantiate the OreoDown framework. Multi-scale context information is obtained through densely connected feature extracting operations. Finally, hybrid dilated convolution is utilized in each layer to further expand the receptive field and solve the problem of rough feature extraction. Four metrics namely Dice similarity coefficient (DSC), Intersection over Union (IoU), 95% Hausdorff surface distance (HSD95) and the average surface distance (ASD) were used to evaluate the segmenting performance of the neural networks. Experiments perform on the public IBSR dataset (18 subjects of images), in which DenseMedic reached 89.2%, 80.7%, 1.982 and 0.882 respectively in 4 metrics; experiments perform on the public MRBrainS18 dataset (7 subjects of images), in which DenseMedic reached 88.7%, 79.8%, 1.249 and 0.570 respectively in 4 metrics. The experimental results show that the segmented subcortical structures and corresponding ground truths have more overlaps in regions and more similarities in outlines, which indicates that DenseMedic can effectively accomplish the segmentation of major subcortical structures. In clinical applications, the presented DenseMedic will help to accurately measure the key indicators for the central nervous system related diseases and provide rapid computer-aided diagnosis and treatment.
杨斌斌, 刘霖雯, 张唯唯. 基于DenseMedic网络的脑皮层下结构的语义分割[J]. 中国生物医学工程学报, 2020, 39(6): 652-666.
Yang Binbin, Liu Linwen, Zhang Weiwei. Semantic Segmentation of Subcortical Brain Structures Based on DenseMedic Network. Chinese Journal of Biomedical Engineering, 2020, 39(6): 652-666.
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