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Related Experiment Video

Updated: Jun 16, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

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

Alec Nieth1, Pritom Karmaker1, Nadia Martinez Naya2,3

  • 1Department of Medical Education, University of Miami Leonard M. Miller School of Medicine, Miami, Florida, USA.

American Journal of Physiology. Heart and Circulatory Physiology
|June 15, 2026
PubMed
Summary

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This summary is machine-generated.

A new Linux-optimized 3D heart analysis pipeline (CODA) accurately maps infiltrative cardiovascular diseases. This tool enhances diagnosis and monitoring of conditions like myocarditis by quantifying tissue damage in 3D.

Area of Science:

  • Biomedical Engineering
  • Digital Pathology
  • Cardiovascular Research

Background:

  • Infiltrative cardiovascular diseases present complex spatial distributions challenging diagnosis and monitoring.
  • Traditional 2D histopathology limits quantitative assessment of disease burden and treatment response.

Purpose of the Study:

  • To present a Linux-optimized, Cellular-resolution Organ Digital Analysis (CODA) pipeline for quantitative 3D reconstruction of whole murine hearts at cellular resolution.
  • To adapt the CODA pipeline for improved accuracy in segmenting affected cardiac tissue and enable visualization in 3D Slicer.

Main Methods:

  • Developed a Linux-optimized CODA pipeline with visualization tools and myocarditis-specific training data.
  • Integrated STL format export for 3D Slicer visualization.
Keywords:
3D reconstructionDeep-LearningHistopathologyMyocarditis

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Last Updated: Jun 16, 2026

3D Whole-heart Myocardial Tissue Analysis
06:53

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Pipeline for Multi-Scale Three-Dimensional Anatomic Study of the Human Heart
04:22

Pipeline for Multi-Scale Three-Dimensional Anatomic Study of the Human Heart

Published on: June 28, 2024

  • Applied the pipeline to a COVID-19-induced myocarditis model (n=6).

    • Main Results:

    • >90% accuracy in segmenting affected tissue in a COVID-19 myocarditis model.
    • Revealed significantly greater volumes of inflammatory and necrotic foci compared to controls.
    • Demonstrated the pipeline's capability for precise 3D tissue analysis.

    Conclusions:

    • The open-source CODA platform offers a scalable, pathologist-AI hybrid workflow for precise 3D cardiac tissue analysis.
    • This tool advances the quantitative assessment of infiltrative cardiovascular diseases.
    • Facilitates improved diagnosis and therapeutic monitoring for conditions like myocarditis.