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.

翻译：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。心肌毛细血管化的定量评估通常涉及对心肌切片中心肌细胞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步标记心肌细胞和毛细血管的简便方法，且背景染色难度较低。然而，后续图像分析仍需人工识别和分割心肌细胞及毛细血管。本研究开发了一款图像分析工具AutoQC，可自动识别并分割IV型胶原（心肌内主要基底膜蛋白）免疫荧光图像中的心肌细胞与毛细血管，并基于分割掩膜计算常用毛细血管化相关测量指标。AutoQC通过提示工程利用预训练分割模型，采用弱监督实例分割算法，在实例分割与毛细血管化评估方面均优于当前最优模型YOLOv8-Seg。此外，AutoQC训练仅需少量带有边界框标注的数据集，无需像素级标注，有效降低了网络训练工作量。该工具为基底膜免疫染色心肌切片的毛细血管化量化提供了自动化解决方案，训练完成后无需人工图像分析。