# Quantification of cardiac capillarization in basement-membrane-immunostained myocardial slices using Segment Anything Model

## TL;DR

This paper introduces AutoQC, an automated tool that uses a pre-trained model to accurately segment and quantify capillaries in heart tissue stained with basement membrane proteins.

## Contribution

The novel contribution is the development of AutoQC, a weakly supervised image analysis tool that outperforms existing models in capillarization assessment with minimal training data.

## Key findings

- AutoQC outperformed SAM and YOLOv8-Seg in instance segmentation and capillarization assessment.
- AutoQC requires only bounding box annotations, reducing training workload and eliminating manual analysis.
- The tool enables high-throughput and high-accuracy assessment of cardiac capillarization.

## 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.