Department of Bioengineering, Clemson University, Clemson, SC, USA.
School of Computing, Clemson University, Clemson, SC, USA.
Sci Rep. 2024 Jul 3;14(1):15344. doi: 10.1038/s41598-024-65567-3.
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 protein is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires expertise and laborious manual work to identify and segment CMs/capillaries. Here, we developed an image analysis tool, AutoQC, for automatic identification and segmentation of CMs and capillaries in immunofluorescence images of basement membrane. Commonly used capillarization-related measurements can be derived from segmentation results. By leveraging the power of a pre-trained segmentation model (Segment Anything Model, SAM) via prompt engineering, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations. AutoQC outperformed SAM (without prompt engineering) and YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Thus, AutoQC, featuring a weakly supervised algorithm, enables automatic segmentation and high-throughput, high-accuracy capillarization assessment in basement-membrane-immunostained myocardial slices. This approach reduces the training workload and eliminates the need for manual image analysis once AutoQC is trained.
心肌毛细血管密度降低已被报道为与各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心肌切片中肌细胞(CMs)和毛细血管的双重免疫染色。相比之下,基底膜蛋白的单次免疫染色是一种简单的方法,可以同时标记 CMs 和毛细血管,在背景染色方面的挑战更少。然而,后续的图像分析总是需要专业知识和繁琐的人工工作来识别和分割 CMs/毛细血管。在这里,我们开发了一种图像分析工具 AutoQC,用于自动识别和分割免疫荧光基底膜图像中的 CMs 和毛细血管。常用的毛细血管化相关测量可以从分割结果中得出。通过利用预训练分割模型(Segment Anything Model,SAM)的功能通过提示工程,AutoQC 的训练仅需要一个带有边界框注释的小数据集,而不是像素级注释。AutoQC 在实例分割和毛细血管化评估方面均优于 SAM(没有提示工程)和最先进的实例分割模型 YOLOv8-Seg。因此,AutoQC 具有弱监督算法,能够在基底膜免疫染色的心肌切片中实现自动分割和高通量、高精度的毛细血管化评估。这种方法减少了训练工作量,并且一旦训练了 AutoQC,就无需进行手动图像分析。