Assessment of the glomerular basement membrane (GBM) in transmission electron
microscopy (TEM) is crucial for diagnosing chronic kidney disease (CKD). The
lack of domain-independent automatic segmentation tools for the GBM
necessitates an AI-based solution to automate the process. In this study, we
introduce GBMSeg, a training-free framework designed to automatically segment
the GBM in TEM images guided only by a one-shot annotated reference.
Specifically, GBMSeg first exploits the robust feature matching capabilities of
the pretrained foundation model to generate initial prompt points, then
introduces a series of novel automatic prompt engineering techniques across the
feature and physical space to optimize the prompt scheme. Finally, GBMSeg
employs a class-agnostic foundation segmentation model with the generated
prompt scheme to obtain accurate segmentation results. Experimental results on
our collected 2538 TEM images confirm that GBMSeg achieves superior
segmentation performance with a Dice similarity coefficient (DSC) of 87.27%
using only one labeled reference image in a training-free manner, outperforming
recently proposed one-shot or few-shot methods. In summary, GBMSeg introduces a
distinctive automatic prompt framework that facilitates robust
domain-independent segmentation performance without training, particularly
advancing the automatic prompting of foundation segmentation models for medical
images. Future work involves automating the thickness measurement of segmented
GBM and quantifying pathological indicators, holding significant potential for
advancing pathology assessments in clinical applications. The source code is
available on this https URL

本研究介绍了一种称为 GBMSeg 的训练 free 框架，旨在通过一次标注的参考图像来自动分割透射电子显微镜中的肾小球基底膜 (GBM)，实验结果表明 GBMSeg 在 2538 个图像上达到了优于其他方法的 87.27% 的 Dice 相似系数 (DSC) 的分割性能，该框架还有潜力在医学图像中自动进行基础分割模型的提醒，未来的工作包括自动测量分割的 GBM 厚度和量化病理指标，对于提升临床应用中的病理学评估具有重要潜力。