Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

The diagnosis of risk level of gastrointestinal stromal tumor (GIST) is of great clinical significance. The morphology of GIST in endoscopic ultrasound (EUS) images has been normally used by radiologists to diagnosis the risk level of GISTs. Hence, accurate segmentation of GISTs in EUS images is a crucial factor to influence the diagnosis. U-net, an elegant network, has been commonly used in medical images. However, due to the plain architecture and complicated up-sampling path of U-net, classical U-net does not perform well in segmenting GISTs in EUS images with diverse size, heavy shadow and ambiguous boundary. Hence, this paper proposes a novel multi-task refined boundary-supervision U-net (MRBSU-net) for GIST segmentation in EUS images. In our network, multi-task refined U-net (RU-net) is set to deal with heavy shadow and diverse size. Boundary cross entropy in loss function of multi-task RU-net boosts the influence of small size tumors and the refinement avoid the noise information in EUS images propagating to the higher resolution layers. Then we design a refined boundary-supervision U-net (RBSU-net) to solve the ambiguous problem. The boundary supervision in RBSU-net leads the network focus on finding boundary in the down-sampling part and segmenting region on the up-sampling path. At last, we put multi-task RU-net in front of the RBSU-net to increase the stability of the network, what is called MRBSU-net. Extensive experiments have been designed to evaluate the performance of the proposed network. The comparison experiments include the results from traditional U-net, generative adversarial network (GAN) and Deep Attentional Features (DAF). The results of our proposed method perform best among all the comparison methods, which proves that the proposed network could be potentially used in clinic.