Deep Learning-Driven 3D Histopathology Method: A Pipeline for Cellular-Resolution Myocarditis Analysis.

Infiltrative cardiovascular diseases are characterized by complex spatial distributions of abnormal tissue that hinder accurate diagnosis and therapeutic monitoring. Conventional 2D 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 3D 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 STL format export for visualization in 3D Slicer. Applied to a COVID-19-induced myocarditis model (n=6), the modified pipeline achieved >90% accuracy in segmenting affected tissue, revealing significantly greater volumes of inflammatory and necrotic foci compared to controls. This open-source platform provides a scalable, pathologist-AI hybrid workflow for precise 3D tissue analysis across infiltrative cardiac diseases.