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Decreased myocardial capillary density has been reported as an importanthistopathological feature associated with various heart disorders. Quantitativeassessment of cardiac capillarization typically involves double immunostainingof cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast,single immunostaining of basement membrane components is a straightforwardapproach to simultaneously label CMs and capillaries, presenting fewerchallenges in background staining. However, subsequent image analysis alwaysrequires manual work in identifying and segmenting CMs and capillaries. Here,we developed an image analysis tool, AutoQC, to automatically identify andsegment CMs and capillaries in immunofluorescence images of collagen type IV, apredominant basement membrane protein within the myocardium. In addition,commonly used capillarization-related measurements can be derived fromsegmentation masks. AutoQC features a weakly supervised instance segmentationalgorithm by leveraging the power of a pre-trained segmentation model viaprompt engineering. AutoQC outperformed YOLOv8-Seg, a state-of-the-art instancesegmentation model, in both instance segmentation and capillarizationassessment. Furthermore, the training of AutoQC required only a small datasetwith bounding box annotations instead of pixel-wise annotations, leading to areduced workload during network training. AutoQC provides an automated solutionfor quantifying cardiac capillarization in basement-membrane-immunostainedmyocardial slices, eliminating the need for manual image analysis once it istrained.

Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation