  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.
