Title | ||
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CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke. |
Abstract | ||
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Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with this task, such as large range of stroke lesion scales and the tissue intensity similarity. The famous encoder-decoder convolutional neural network, which although has made great achievements in medical image segmentation areas, may fail to address these challenges due to the insufficient uses of multi-scale features and context information. To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images. Specifically, a Cross-Level feature Fusion (CLF) strategy was developed to make full use of different scale features across different levels; Extending Atrous Spatial Pyramid Pooling (ASPP) with CLF, we have enriched multi-scale features to handle the different lesion sizes; In addition, convolutional long short-term memory (ConvLSTM) is employed to infer context information and thus capture fine structures to address the intensity similarity issue. The proposed approach was evaluated on an open-source dataset, the Anatomical Tracings of Lesions After Stroke (ATLAS) with the results showing that our network outperforms five state-of-the-art methods. We make our code and models available at https://github.com/YH0517/CLCINet. |
Year | DOI | Venue |
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2019 | 10.1007/978-3-030-32248-9_30 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Deep learning,Chronic stroke,Segmentation,Cross-Level | Conference | 11766 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hao Yang | 1 | 3 | 1.40 |
Weijian Huang | 2 | 2 | 1.05 |
Kehan Qi | 3 | 3 | 1.07 |
Cheng Li | 4 | 4 | 3.10 |
Xinfeng Liu | 5 | 0 | 1.35 |
Meiyun Wang | 6 | 4 | 1.10 |
Hairong Zheng | 7 | 56 | 28.24 |
Shanshan Wang | 8 | 0 | 0.34 |