| Title |
Detection of collagen band–associated regions in H&E-stained colonic biopsies of collagenous colitis patients using superpixel-based feature extraction and neural network classification |
| Authors |
Kiudelis, Vytautas ; Petrolis, Robertas ; Ramonaitė, Rima ; Jančiauskas, Dainius ; Poškienė, Lina ; Kupčinskas, Juozas ; Šabanas, Povilas ; Čerapaitė-Trušinskienė, Reda ; Meilutytė-Lukauskienė, Diana ; Kriščiukaitis, Algimantas |
| DOI |
10.1186/s13000-026-01783-x |
| Full Text |
|
| Is Part of |
Diagnostic Pathology.. Springer Science and Business Media LLC. 2026, p. 1-24 |
| Keywords [eng] |
Collagenous colitis (CC) ; Collagen ; Histopathology ; Superpixels ; Deep learning ; Machine-learning-based. |
| Abstract [eng] |
Background Collagenous colitis (CC) is diagnosed histologically and is characterised by a thickened subepithelial collagen band together with inflammatory and epithelial changes. Although routine haematoxylin and eosin (H&E) staining is sufficient for diagnosis in most cases, visual assessment of the collagen band can be challenging in borderline or heterogeneous specimens. Additional stains may be required in diagnostically difficult situations. The aim To develop a machine-learning–based algorithm for detecting subepithelial collagen band-associated regions in routine H&E-stained colonic biopsy images as a decision-support tool for histopathological assessment. Methods H&E-stained colonic biopsy specimens from 36 patients with histologically confirmed CC were imaged at 20×magnification (1392×1040 pixels). Images were segmented into 1,000 superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm. Superpixels overlapping with expert-provided rough annotations of the collagen band were labelled and characterised using normalised RGB histograms. A feed-forward neural network classifier (three hidden layers, 10 neurons per layer) was trained to distinguish collagen band–associated from non-collagen regions. Class imbalance was addressed by data augmentation of minority-class superpixels. Post-processing with connected-component size filtering was applied to enforce spatial continuity. Superpixel-level performance was evaluated quantitatively, and image-level outputs were assessed using expert acceptability scoring. |
| Published |
Springer Science and Business Media LLC |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
