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arXiv · 2011.08769

Anatomy Prior Based U-net for Pathology Segmentation with Attention

Yuncheng Zhou·Ke Zhang·Xinzhe Luo·Sihan Wang·Xiahai Zhuang
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Abstract

Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and small area, pathological segmentation has always been a challenging task. We propose an anatomy prior based framework, which combines the U-net segmentation network with the attention technique. Leveraging the fact that the pathology is inclusive, we propose a neighborhood penalty strategy to gauge the inclusion relationship between the myocardium and the myocardial infarction and no-reflow areas. This neighborhood penalty strategy can be applied to any two labels with inclusive relationships (such as the whole infarction and myocardium, etc.) to form a neighboring loss. The proposed framework is evaluated on the EMIDEC dataset. Results show that our framework is effective in pathological area segmentation.

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Yuncheng Zhou, Ke Zhang, Xinzhe Luo, Sihan Wang, Xiahai Zhuang. 2020-11-17. Anatomy Prior Based U-net for Pathology Segmentation with Attention. https://arxiv.org/abs/2011.08769

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