Semi-Supervised Instrument Segmentation for Endoscopic Spinal Surgery

Segmentation tasks require multiple annotation work which is time-consuming and labour-intensive. How to make full use of unlabelled data to assist in training deep learning models has been a research hotspot in recent years. This paper takes instrument segmentation in endoscopic surgery as the background to explore how to use unlabelled data for semi-supervised learning more reasonably and effectively. An adaptive gradient correction method based on the degree of perturbation is proposed to improve segmentation accuracy. This paper integrates the recently popular segment anything model (SAM) with semi-supervised learning, taking full advantage of the large model to enhance the zero-shot ability of the model. Experimental results demonstrate the superior performance of the proposed segmentation strategy compared to traditional semi-supervised segmentation methods, achieving a 2.56% improvement in mean intersection over union (mIoU). The visual segmentation results show that incorporation of SAM significantly enhances our method, resulting in more accurate segmentation boundaries.