FibroTrack: a standalone deep learning platform for automated fibrosis quantification in muscle and cardiac histology

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 streamlines fibrosis analysis across Sirius Red (SR), Masson's 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 99.5% mask precision for muscle segmentation 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.