LR-Net: A Multi-task Model Using Relationship-based Contour Information to Enhance the Semantic Segmentation of Cancer Regions
Baorong Shi, Hong Zhang, Rui Yan, Wang Jing, Jinfeng Zang, Fa Zhang · 2020
The segmentation of cancer regions is a key step in pathological image analysis. Although traditional methods (such as U-Net) have achieved good results in general medical image segmentation, the segmentation performance of the tumor region is still unsatisfactory because the boundary of the tumor is too blurred. Moreover, most tumor region segmentation methods focus on the learning of image content features while ignoring learning relationship among pixels on tumor contours. In this paper, we developed a multi-task learning technique to enhance the importance of contours and increase the weight of pixels relationship learning for the tumor segmentation. Different from the traditional single-decoder network, a parallel contour decoder with LRLM (location relationship learning module) is introduced as an auxiliary decoder to learn the relationship-based features of tumor contours, which forms a two-decoder network. To promote the information fusion of the two tasks, the two decoders share a same encoder with bidirectional skip connections between the auxiliary contour decoder and the main content decoder. Experimental results show that LR-Net is superior to many popular approaches, such as CE-Net and U-Net.