Work

Automatic segmentation of histological zones in rat distraction osteogenesis using a self-configuring deep learning pipeline

Year: 2026

Type: article

Abstract: Distraction osteogenesis regenerates bone by gradually separating bone segments and inducing new tissue formation within the distraction gap. Although histologic assessment of regenerate composition is essential for evaluating tissue maturation, manual delineation of tissue compartments is labor-intensive and subject to observer variability. This study evaluated a deep learning-based workflow for the automated segmentation of three histologic zones in a rat distraction osteogenesis model: the fibrous interzone, the microcolumn formation zone, and the remodeling zone. High-resolution hematoxylin and eosin- and pentachrome-stained sections were used to train two-dimensional U-Net models in multi-class and single-class settings with five-fold cross-validation and a held-out test set. Performance was assessed using the Dice score, precision, and recall, and was supplemented by a qualitative review of the prediction overlays. On the test set, segmentation was most reliable for the fibrous interzone (Dice 0.76 in H&E; 0.75 in pentachrome), followed by the microcolumn formation zone (Dice 0.63; 0.57). The remodeling zone (Dice 0.29; 0.44) was the most challenging compartment due to its variable and late-stage appearance. Due to incomplete and partly ambiguous ground truth, quantitative metrics were interpreted alongside qualitative histologic assessment. Under this combined evaluation, the pentachrome multi-class configuration yielded the most coherent results. These findings suggest that deep learning-assisted segmentation is a reproducible approach for histologic compartment analysis in distraction osteogenesis. Beyond this application, the evaluated pipeline provides a practical workflow for related histologic segmentation tasks. Implementation in 3D Slicer enables inference, visualization, and area-based quantification in an accessible format for translational research.

Source: Scientific Reports

DOI: 10.1038/s41598-026-76895-x

Authors: Leonie Dittrich, Jan-Moritz Ramge, Claudia Neunaber, Dominik Wolff

Institutions: Medizinische Hochschule Hannover, Technische Universität Braunschweig

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690