Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM's segmentation accuracy for renal pathology.