Pan Shengyu, Tang Xuanli, Chen Bingxian, Lai Xiaobo, Jin Wei
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Zhejiang, Hangzhou, China.
Department of Nephrology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
PLoS One. 2025 Apr 14;20(4):e0321096. doi: 10.1371/journal.pone.0321096. eCollection 2025.
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.
在肾脏病学研究中，免疫荧光图像中的多肾小球分割在慢性肾脏病的早期检测和诊断中起着至关重要的作用。然而，获得准确的分割结果通常需要耗费大量人力的标注，并且现有方法受到召回率低和准确性有限的阻碍。最近，一种通用的分割一切模型（SAM）在多个分割任务中展现出了有前景的性能。本文引入了一种基于SAM的多肾小球分割模型（GlomSAM），以将SAM应用于免疫荧光病理学领域。利用卷积网络和具有提示的变压器结构的优势的融合编码器策略被用于促进SAM在病理分析应用中的迁移学习。此外，使用一个粗略掩码生成器来生成初步的肾小球分割掩码，实现自动输入提示并改善最终分割结果。广泛的对比实验和消融研究表明其具有超越其他相关研究的先进性能。我们希望本报告能为推进肾小球分割领域的发展提供见解，并在未来促进更多有趣的工作。
