Semi-Supervised Learning With Fact-Forcing for Medical Image Segmentation

Precise and robust image segmentation is one of the most important steps in supervised deep learning-applied studies. Especially in the medical field, image segmentation requires an enormous time and professionals with clinical knowledge. Although there are constant attempts for automatic and semi-automatic image segmentation algorithm development, acquiring not only clinically accurate but also precise pixel-level annotations for medical images remains insufficient. This article presents a semi-supervised learning method with a novel fact-forcing process, referred to as FFSS, to reduce the labeling cost while improving the prediction accuracy for medical image segmentation. FFSS includes two components: a pre-trained teacher and a student that would be trained, iteratively. In each iteration, the teacher first generates a pseudo-label for each image in an unlabeled set, the student is then trained on the pseudo-labeled set and sends feedback to update the teacher. A fact-forcing process is designed to improve the quality of the student model using a labeled set. We have comprehensively evaluated our method on both three-dimensional binary segmentation and two-dimensional multi-class segmentation. The evaluation results demonstrate significant accuracy improvements of FFSS compared with the state-of-the-art semi-supervised methods. Due to the fact-forcing process, the proposed method consistently outperforms the other ones under various labeled data ratios for all benchmark datasets, including left atrium MRI, pancreas CT, ACDC MRI, and OCT. By refining the quality of student feedback with complementary supervised training, the proposed FFSS shows robustness under labeled data scarcity for diverse types of medical images.