Automated segmentation and quantification of histological liver features for MASH/MASLD scoring
Abstract
The increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective histopathological assessment. Conventional histological approaches are time-consuming and subjective. Therefore, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features of MASH/MASLD. Segmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. Our qHisto (quantitative histology) method, is nnU-Net-based and quantifies key histological components of the MASH-score, including steatosis, fibrosis, inflammation and ballooning. Additionally, we determined novel features that are inaccessible through manual histology, like the distribution of fat droplet sizes, nuclear aspect ratio and heatmaps. qHisto parameters showed strong positive correlations with conventional histology scores (fat: R = 0.91, inflammation: R = 0.70, ballooning: R = 0.47) and with hepatic fat fraction by magnetic resonance imaging ( R = 0.87). Our novel scores showed that nuclei deformation is driven by large fat droplets rather than the overall amount of fat. A key advantage of our method is spatially resolved, precise histological quantification to provide detailed assessment of disease severity. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide analysis that supports histopathologists and enhances clinical assessment.
Related
- Immunophenotypic Features of T-Lymphocyte Distribution Across Histological and Molecular Subtypes of Serous Ovarian Neoplasms: A Retrospective Immunohistochemical Study
- Automatic segmentation of histological zones in rat distraction osteogenesis using a self-configuring deep learning pipeline
- BanffNET, a Deep Learning System for Comprehensive Histological Lesion Quantification in Kidney Transplant Biopsies
- A de novo gliosarcoma of the posterior fossa in a young patient with unique histological features: A rare case report
- Automated thigh muscle segmentation using foundation models: validation and impact of segmentation quality
BibTeX
@article{spirgath2026automated,
title = {{Automated segmentation and quantification of histological liver features for MASH/MASLD scoring}},
author = {Kristin Spirgath and Biru Huang and Yasmine Safraou and Mohamed Dahami and Max Kraftberger and Tim-Rasmus Kiehl and Clara H. F. Stockburger and Christian Bayerl and Jakob Ludwig and Noah Jaitner and Anja A. Kühl and Patrick Asbach and Dominik Geisel and Karl H. Hillebrandt and Rebecca G. Wells and Ingolf Sack and Heiko Tzschätzsch},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76675-7},
url = {https://www.nature.com/articles/s41598-026-76675-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Automated segmentation and quantification of histological liver features for MASH/MASLD scoring
AU - Kristin Spirgath
AU - Biru Huang
AU - Yasmine Safraou
AU - Mohamed Dahami
AU - Max Kraftberger
AU - Tim-Rasmus Kiehl
AU - Clara H. F. Stockburger
AU - Christian Bayerl
AU - Jakob Ludwig
AU - Noah Jaitner
AU - Anja A. Kühl
AU - Patrick Asbach
AU - Dominik Geisel
AU - Karl H. Hillebrandt
AU - Rebecca G. Wells
AU - Ingolf Sack
AU - Heiko Tzschätzsch
PY - 2026
DA - 2026/10/27
JO - Scientific Reports
DO - 10.1038/s41598-026-76675-7
UR - https://www.nature.com/articles/s41598-026-76675-7
AB - The increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective histopathological assessment. Conventional histological approaches are time-consuming and subjective. Therefore, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features of MASH/MASLD. Segmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. Our qHisto (quantitative histology) method, is nnU-Net-based and quantifies key histological components of the MASH-score, including steatosis, fibrosis, inflammation and ballooning. Additionally, we determined novel features that are inaccessible through manual histology, like the distribution of fat droplet sizes, nuclear aspect ratio and heatmaps. qHisto parameters showed strong positive correlations with conventional histology scores (fat: R = 0.91, inflammation: R = 0.70, ballooning: R = 0.47) and with hepatic fat fraction by magnetic resonance imaging ( R = 0.87). Our novel scores showed that nuclei deformation is driven by large fat droplets rather than the overall amount of fat. A key advantage of our method is spatially resolved, precise histological quantification to provide detailed assessment of disease severity. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide analysis that supports histopathologists and enhances clinical assessment.
SN - 2045-2322
ER -