Abstract
In nephrology research, multi-glomerular segmentation in immunofluorescence images plays a crucial role in the early detection and diagnosis of chronic kidney disease. However, obtaining accurate segmentations often requires labor-intensive annotations and existing methods are hampered by low recall rates and limited accuracy. Recently, a general Segment Anything Model (SAM) has demonstrated promising performance in several segmentation tasks. In this paper, a SAM-based multi-glomerular segmentation model (GlomSAM) is introduced to employ SAM in the immunofluorescence pathology domain. The fusion encoder strategy utilizing the advantages of both convolution networks and transformer structures with prompts is conducted to facilitate SAM's transfer learning in applications of pathological analysis. Moreover, a rough mask generator is employed to generate preliminary glomerular segmentation masks, enabling automated input prompting and improving the final segmentation results. Extensive comparative experiments and ablation studies show its state-of-the-art performance surpassing other relevant research. We hope this report will provide insights to advance the field of glomerular segmentation and promote more interesting work in the future.