Ablation study results.

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.

在肾脏病学（nephrology）研究中，免疫荧光图像（immunofluorescence images）的多肾小球分割对于慢性肾脏病（chronic kidney disease）的早期检测与诊断具有至关重要的作用。然而，获取精准的分割结果往往需要耗时耗力的人工标注，且现有方法存在召回率偏低、准确率受限的问题。近期，通用型分割一切模型（Segment Anything Model，SAM）在多项分割任务中展现出了出色的性能。本文提出了一种基于SAM的多肾小球分割模型（GlomSAM），旨在将SAM应用于免疫荧光病理领域。本研究采用融合编码器策略，结合卷积神经网络（convolution networks）与带提示词的Transformer架构的优势，以促进SAM在病理分析任务中的迁移学习。此外，本文还引入了粗略掩码生成器以生成初步的肾小球分割掩码，实现自动化的输入提示生成，进而优化最终的分割效果。大量对比实验与消融研究（ablation studies）表明，该模型的性能达到当前最优水准，优于其他相关研究成果。我们期望本报告能够为肾小球分割领域的发展提供新思路，并推动未来更多富有价值的相关研究工作。