ArticleAmerican journal of physiology. Heart and circulatory physiology2026
Deep learning-driven 3-D histopathology method: a pipeline for cellular-resolution myocarditis analysis.
Article in American journal of physiology. Heart and circulatory physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Infiltrative cardiovascular diseases are characterized by complex spatial distributions of abnormal tissue that hinder accurate diagnosis and therapeutic monitoring. Conventional two-dimensional (2-D) histopathological analysis often fails to capture their full extent, limiting quantitative assessment of disease burden and treatment response. We present a Linux-optimized, Cellular-resolution Organ Digital Analysis (CODA) pipeline for quantitative three-dimensional (3-D) reconstruction of whole murine hearts at cellular resolution. Our adaptations ensure compatibility with Linux environments, incorporate visualization tools for integration with external software, and integrate myocarditis-specific training data to improve segmentation accuracy, along with the standard triangle language (STL) format export for visualization in 3-D Slicer. Applied to a COVID-19-induced myocarditis model (
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