Semi-supervised abdominal multi-organ segmentation via dual-task de-biased consistency

Abdominal multi-organ segmentation suffers from the problems of unbalanced classes and difficult learning of dynamic organs, which leads to the segmentation effect being seriously affected. We present a dual-task de-bias consistency semi-supervised framework for the segmentation of several abdominal organs. First, multi-class hausdorff distance loss is proposed for unsupervised loss. This loss is more sensitive to shapes and boundaries, focusing on the distance of the maximum error. It effectively captures boundary errors of small classes and enhances overall segmentation performance by balancing the learning of each class. Secondly, a dual debiasing strategy is proposed for the two task branches of pixel and contour prediction, dynamically adjusting for data bias and learning bias. It adjusts the focus of attention timely to help the model learn small and difficult classes. Finally, experiments conducted on two different datasets demonstrate that the proposed model achieves optimal performance and can effectively improve the accuracy of small classes. On the Synapse dataset with 10% labels, our model achieves an improvement of 6.9 in avg DSC.