Learning with Explicit Shape Priors for Medical Image Segmentation
This work addresses segmentation challenges in medical imaging, offering a plug-and-play solution that improves accuracy for tasks like organ or tumor delineation, though it appears incremental by building on existing UNet and Transformer backbones.
The authors tackled the limitations of UNet-based medical image segmentation models, which struggle with long-range dependencies and heavy reliance on segmentation heads, by proposing a shape prior module (SPM) that introduces explicit global and local shape priors, achieving state-of-the-art performance on three public datasets.
Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolution-neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these models are heavily dependent on the training of the final segmentation head. And existing methods can not well address these two limitations at the same time. Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models. The explicit shape priors consist of global and local shape priors. The former with coarse shape representations provides networks with capabilities to model global contexts. The latter with finer shape information serves as additional guidance to boost the segmentation performance, which relieves the heavy dependence on the learnable prototype in the segmentation head. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM shows an outstanding generalization ability on classic CNNs and recent Transformer-based backbones, which can serve as a plug-and-play structure for the segmentation task of different datasets. Source codes are available at https://github.com/AlexYouXin/Explicit-Shape-Priors