CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer.

Area of Science:

• Cardiovascular pathology
• Biomedical imaging
• Machine learning in histology

Background:

• Connexin-43 (CX43) lateralization in ventricular myocardium is linked to abnormal impulse propagation and arrhythmia susceptibility.
• Quantitative assessment of CX43 lateralization in histological sections is challenging due to complex segmentation requirements.
• Existing methods rely on segmenting individual cardiomyocytes and applying geometric rules, limiting scalability and accuracy.

Purpose of the Study:

• To introduce CLARISA, a novel deep learning framework for segmentation-free, region-of-interest (ROI)-based classification of CX43 lateralization.
• To automate the quantitative assessment of CX43 distribution in cardiac tissue, facilitating arrhythmia risk stratification.
• To develop a method that reduces the annotation burden compared to traditional cell-segmentation approaches.

Main Methods:

• Developed CLARISA, a deep learning framework utilizing a dual-stream EfficientNetV2-S classifier for ROI-based CX43 lateralization classification.
• Generated an expert-annotated dataset from Wistar rat heart cryosections, labeling CX43-positive regions based on distribution patterns.
• Implemented a semi-automated whole-section inference module for generating spatial lateralization probability maps and global estimates.

Main Results:

• CLARISA achieved high performance on a held-out test set with ROC-AUC of 0.904 and PR-AUC of 0.808.
• Whole-section analysis generated spatial maps reflecting heterogeneous CX43 organization and provided global estimates closely aligned with expert annotations.
• The ROI-based approach demonstrated a non-equivalent but related readout to cell-segmentation methods, with reduced annotation complexity.

Conclusions:

• CLARISA represents a proof-of-principle for scalable, segmentation-free assessment of CX43 lateralization in cardiac tissue.
• The framework simplifies quantitative analysis of CX43 distribution, offering potential for improved arrhythmia risk assessment.
• Further validation on diverse datasets is needed to confirm robustness, portability, and translational applicability.