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Abstract #3197

Accurate Prostate Segmentation in MR Images Guided by Semantic Flow

Yousuf Babiker M. Osman1, Cheng Li1, Zhenzhen Xue1, Hairong Zheng1, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China


To enlarge the receptive field, downsampling is frequently utilized in deep learning (DL) models. Consequently, there exists one common issue for DL-based image segmentation – the misalignment between high-resolution features and high-semantic features. To this end, decoding or upsampling has been proposed and promising performances have been achieved. However, upsamling without explicit pixel-wise localization guidance may introduce errors. To address this issue, we propose a semantic flow-guided prostate segmentation method. By guiding the upsampling process with semantic flow calculated from both high-resolution and high-semantic features, more accurate segmentation results are generated.

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