Medical image segmentation is considered as the basic step for medical image analysis and surgical intervention. And many previous works attempted to incorporate shape priors for designing segmentation models, which is beneficial to attain finer masks with anatomical shape information. Here in our work, we detailedly discuss three types of segmentation models with shape priors, which consist of atlas-based models, statistical-based models and UNet-based models. On the ground that the former two kinds of methods show a poor generalization ability, UNet-based models have dominated the field of medical image segmentation in recent years. However, existing UNet-based models tend to employ implicit shape priors, which do not have a good interpretability and generalization ability on different organs with distinctive shapes. Thus, we proposed a novel shape prior module (SPM), which could explicitly introduce shape priors to promote the segmentation performance of UNet-based models. 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 different classic convolution-neural-networks (CNNs) and recent Transformer-based backbones, which can serve as a plug-and-play structure for the segmentation task of different datasets.

翻译：医学图像分割被认为是医学图像分析和手术干预的基础步骤。许多先前的工作尝试在分割模型中引入形状先验，这有利于获得包含解剖形状信息的更精细掩膜。在本工作中，我们详细讨论了三种带有形状先验的分割模型，包括基于图谱的模型、基于统计的模型和基于UNet的模型。鉴于前两类方法泛化能力较差，基于UNet的模型近年来主导了医学图像分割领域。然而，现有的UNet模型倾向于使用隐式形状先验，这使得它们在不同形状的器官上缺乏良好的可解释性和泛化能力。因此，我们提出了一种新颖的形状先验模块（SPM），该模块可以显式引入形状先验以提升UNet模型的分割性能。为了评估SPM的有效性，我们在三个具有挑战性的公开数据集上进行了实验，所提出的模型达到了最先进的性能。此外，SPM在不同经典卷积神经网络（CNN）和近期基于Transformer的主干网络上展现出卓越的泛化能力，可作为即插即用结构应用于不同数据集的分割任务。