Related Experiment Video
Updated: Jun 22, 2025
3D Whole-heart Myocardial Tissue Analysis
Published on: April 12, 2017
Quantification of cardiac capillarization in basement-membrane-immunostained myocardial slices using Segment Anything
Zhao Zhang1, Xiwen Chen2, William Richardson3
1Department of Bioengineering, Clemson University, Clemson, SC, USA.
Insights
A new tool, AutoQC, automates the analysis of heart capillary density from basement membrane images. This weakly supervised method enhances accuracy and efficiency in assessing cardiac capillarization for heart disorder research.
Area of Science:
- Cardiovascular pathology
- Biomedical image analysis
- Computational biology
Background:
- Decreased myocardial capillary density is a key feature in heart disorders.
- Assessing cardiac capillarization typically requires complex double immunostaining and manual image analysis.
- Single basement membrane immunostaining offers a simpler approach but still demands laborious segmentation.
Purpose of the Study:
- To develop an automated image analysis tool, AutoQC, for segmenting cardiomyocytes and capillaries.
- To enable high-throughput and accurate assessment of cardiac capillarization using basement membrane staining.
- To reduce the need for manual labor and expertise in myocardial capillarization analysis.
Main Methods:
- Developed AutoQC using a pre-trained segmentation model (Segment Anything Model, SAM) with prompt engineering.
- Utilized a weakly supervised learning approach requiring only bounding box annotations for training.
- Compared AutoQC's performance against SAM (without prompt engineering) and YOLOv8-Seg for instance segmentation and capillarization assessment.
Main Results:
- AutoQC achieved superior performance in both instance segmentation and capillarization assessment compared to SAM and YOLOv8-Seg.
- The tool enables automatic identification and segmentation of cardiomyocytes and capillaries in basement membrane-stained myocardial slices.
- Weakly supervised training significantly reduced the data annotation workload.
Conclusions:
- AutoQC provides a high-throughput, high-accuracy solution for assessing cardiac capillarization.
- This automated approach simplifies image analysis for basement membrane-stained myocardial tissue.
- AutoQC reduces training requirements and eliminates manual image analysis, accelerating heart disorder research.
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.
