Semi-Supervised Small Intestinal Tumor CT Image Segmentation Using Dual-Level Consistency Regularization

To address the challenges of small intestinal tumor CT image labeling with a small number of data, class imbalance, and low segmentation accuracy. This paper introduces Dulecr-Net, a semi-supervised segmentation model. The model first employs the PolyMix algorithm combined with a dual-level consistency regularization approach to address the challenge of class imbalance in CT images of small intestinal tumors. Second, we designed a novel segmentation network, MSA-RFSEU-Net, in order to achieve high-precision segmentation of the lesion region in CT images of small intestinal tumors, by performing experimental analysis on the dataset provided by the Affiliated Hospital of Qingdao University. With only 15% labeled data, The Dice and Jaccard coefficients of the method reached 85.12% and 82.16% respectively, showing improvements of 19.86% and 28.62% compared to the fully supervised U-Net. The experimental results show that Dulecr-Net has a great performance improvement in the effectiveness of medical image segmentation compared with fully supervised and semi-supervised methods, and can effectively address imbalances in lesion area classes.