Overlay Mantle-Free for Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation, crucial for medical research, enhances model generalization using unlabeled data with minimal labeled data. Current methods face edge uncertainty and struggle to learn specific shapes from pixel classification alone. To address these issues, we proposed two-stage knowledge distillation approach employs a teacher model to distill information from labeled data, enhancing the student model with unlabeled data. In the first stage, we use true labels to augment data and sharpen target edges to make teacher predictions more confident. In the second stage, we freeze the teacher model parameters to generate pseudo labels for unlabeled data and guide the student model to learn. By feeding the original background image to the teacher and the enhanced image to the student, The student model learns the information hidden under the mantle and the overall shape of hidden information of the segmented target. Experimental results on the Left Atrium dataset surpass existing methods. Our Overlay Mantle-Free training method enables segmentation based on learned shape information even in data loss scenarios, exhibiting improved edge segmentation accuracy.The code is available at https://github.com/vigilliu/OMF .