CPSS-Net: A Cross-pseudo Semi-supervised Network for Liver Vessel Segmentation from CTA Images

Accurate segmentation of liver vessel from CTA images is often challenging due to the limited availability of labeled data. In the field of medical image segmentation, semi-supervised learning has garnered significant attention as it utilizes unlabeled data to enhance the training of segmentation models. In this paper, we propose a novel cross-pseudo semi-supervised network (CPSS-Net) based on nnU-Net. The CPSS-Net contains three innovative components: 1) An probability prediction (PP) module is designed to generate probability maps for both labeled and unlabeled datasets, capturing model uncertainty through parallel nnU-Net; 2) A double pseudo-label (DPL) module is used to convert the predicted probability maps into double soft pseudo-labels using an adaptive sharpening function; 3) A cross pseudo-supervised (CPS) module is introduced to learn the mutual consistency of double pseudo-labels. Test experiments on both public and private datasets show that our method achieved a Dice score of 0.67 and a sensitivity score of 0.69, surpassing the segmentation accuracy of existing related methods.