使用弱监督实例分割对单免疫染色心肌切片中的心脏毛细血管化进行定量分析
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation
AI总结：
本文开发了弱监督实例分割工具AutoQC，利用提示工程和预训练分割模型，仅需边界框标注即可自动识别心肌细胞和毛细血管，在性能上超越YOLOv8-Seg，实现了心脏毛细血管化的自动化定量分析。
摘要：
心肌毛细血管密度降低已被报道为与多种心脏疾病相关的重要组织病理学特征。心脏毛细血管化的定量评估通常涉及对心肌切片中的心肌细胞（CMs）和毛细血管进行双重免疫染色。相比之下，基底膜成分的单免疫染色是一种同时标记心肌细胞和毛细血管的直接方法，在背景染色方面面临的挑战较少。然而，后续的图像分析在识别和分割心肌细胞与毛细血管时始终需要人工操作。在此，我们开发了一种图像分析工具AutoQC，用于自动识别和分割IV型胶原蛋白（心肌内一种主要的基底膜蛋白）免疫荧光图像中的心肌细胞和毛细血管。此外，常用的毛细血管化相关测量指标可以从分割掩膜中推导得出。AutoQC采用弱监督实例分割算法，通过提示工程利用预训练分割模型的能力。AutoQC在实例分割和毛细血管化评估两方面均优于最先进的实例分割模型YOLOv8-Seg。此外，AutoQC的训练仅需要带有边界框标注的小型数据集，而无需像素级标注，从而减少了网络训练期间的工作量。AutoQC为基底膜免疫染色心肌切片中的心脏毛细血管化定量提供了一种自动化解决方案，一旦训练完成，便无需再进行人工图像分析。
英文摘要：
Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane components is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires manual work in identifying and segmenting CMs and capillaries. Here, we developed an image analysis tool, AutoQC, to automatically identify and segment CMs and capillaries in immunofluorescence images of collagen type IV, a predominant basement membrane protein within the myocardium. In addition, commonly used capillarization-related measurements can be derived from segmentation masks. AutoQC features a weakly supervised instance segmentation algorithm by leveraging the power of a pre-trained segmentation model via prompt engineering. AutoQC outperformed YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Furthermore, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations, leading to a reduced workload during network training. AutoQC provides an automated solution for quantifying cardiac capillarization in basement-membrane-immunostained myocardial slices, eliminating the need for manual image analysis once it is trained.
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。
