特征提示式GBMSeg：用于肾小球基底膜分割的单样本参考引导免训练提示工程
Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation
AI总结：
针对肾小球基底膜自动分割工具缺失的问题，提出免训练框架GBMSeg，基于单样本参考结合自动提示工程实现TEM图像精准分割，性能优于现有单/少样本方法，推动医学图像基础模型自动提示发展。
摘要：
在透射电子显微镜（TEM）下评估肾小球基底膜（GBM）对慢性肾脏病（CKD）的诊断至关重要。由于缺乏跨领域通用的GBM自动分割工具，亟需基于人工智能的方案实现该流程自动化。本研究提出GBMSeg，这是一种免训练框架，仅需一张带标注的单样本参考图像，即可自动分割TEM图像中的GBM。具体而言，GBMSeg首先利用预训练基础模型的强大特征匹配能力生成初始提示点，随后在特征空间和物理空间中引入一系列全新的自动提示工程技术优化提示方案。最后，GBMSeg采用类别无关的基础分割模型，结合生成的提示方案获得精准分割结果。在我们收集的2538张TEM图像上的实验结果证实，GBMSeg仅使用一张标注参考图像，以免训练方式实现了87.27%的戴斯相似系数（DSC），分割性能优于近期提出的单样本或少样本方法。综上，GBMSeg提出了一种独特的自动提示框架，无需训练即可实现稳健的跨领域分割性能，尤其推动了医学图像基础分割模型的自动提示技术发展。未来工作将实现分割后GBM厚度测量的自动化及病理指标量化，在推动临床病理评估应用方面具有重要潜力。源代码已发布于https://github.com/SnowRain510/GBMSeg。
英文摘要：
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 https://github.com/SnowRain510/GBMSeg
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
