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bioRxiv · 10.1101/2025.09.02.673720

FibroTrack: A Standalone Deep Learning Platform for Automated Fibrosis Quantification in Muscle and Cardiac Histology

Odeh, A.·Salem, R.·Abu Saleh, M.·Shemesh, A.·Stein, P.·Livneh, I.·Hasson, P.
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Abstract

Accurate fibrosis quantification is essential for understanding muscle and cardiac disease, yet current manual and semi-automated methods remain slow, subjective, and poorly reproducible. We introduce FibroTrack, a standalone deep learning platform with a graphical user interface (GUI) that fully automates fibrosis analysis across Sirius Red (SR), Massons Trichrome (MT), and immunohistochemistry (IHC) stainings. FibroTrack uniquely integrates LAB (lightness, green-red, blue-yellow) color space normalization with a You Only Look Once version 11 (YOLOv11) segmentation model trained on 2,034 histological images. This approach achieved >97% precision and demonstrated excellent concordance with blinded pathologists (Spearman correlation, r = 0.87-0.96). Automated outputs include segmented images and structured spreadsheets, ensuring high reproducibility and scalability. By combining advanced color analysis with state-of-the-art segmentation in an accessible tool, FibroTrack provides a novel, accurate, and clinically relevant solution for high-throughput fibrosis quantification in both preclinical research and pathology practice.

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Odeh, A., Salem, R., Abu Saleh, M., Shemesh, A., Stein, P., Livneh, I., Hasson, P.. 2025-09-07. FibroTrack: A Standalone Deep Learning Platform for Automated Fibrosis Quantification in Muscle and Cardiac Histology. https://doi.org/10.1101/2025.09.02.673720

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