PDF(Pubmed)
Abstract：
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.
摘要：
据报道，心肌毛细血管密度降低是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心肌细胞(CM)和毛细血管的双重免疫染色。相比之下,基底膜蛋白的单一免疫染色是同时标记CMs和毛细血管的简单方法，在背景染色中呈现较少的挑战。然而,随后的图像分析总是需要专业知识和费力的手工工作来识别和分割CM/毛细血管。这里,我们开发了一个图像分析工具，AutoQC,用于基底膜免疫荧光图像中CM和毛细血管的自动识别和分割。可以从分割结果导出常用的毛细管化相关测量。通过利用预先训练的分割模型(SegmentAnythingModel,SAM)通过即时工程，AutoQC的训练只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的性能优于SAM(没有及时的工程)和YOLOv8-Seg,最先进的实例分割模型，在实例分割和毛细管化评估中。因此，AutoQC,具有弱监督算法，实现自动分割和高吞吐量，在基底膜免疫染色的心肌切片中进行高精度毛细管化评估。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。
