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Abstract #3514

Multi-scale Entity Encoder-decoder Network Learning for Stroke Lesion Segmentation

Hao Yang1, Kehan Qi1, Xin Yu2, Hairong Zheng1, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Inst. of Advanced Technology, Shenzhen, China, 2Case Western Reserve University, Cleveland, OH, United States

The encoder-decoder structure have demonstrated encouraging progress in biomedical image segmentation. Nevertheless, there are still many challenges related to the segmentation of stroke lesions, including dealing with diverse lesion locations, variations in lesion scales, and fuzzy lesion boundaries. In order to address these challenges, this paper proposes a deep neural network architecture denoted as the Multi-Scale Deep Fusion Network (MSDF-Net) with Atrous Spatial Pyramid Pooling (ASPP) for the feature extraction at different scales, and the inclusion of capsules to deal with complicated relative entities. Experimental results shows that the proposed model achieved a higher evaluating score compared to 5 models.

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