Fibrosis detection and quantification in whole slide images through deep learning

Davide Panzeri; Elena Pagani; Riccardo Scodellaro; Giuseppe Chirico; Luca Di Tommaso; Donato Inverso; Laura Sironi

doi:10.1117/12.2673779

12 August 2023 Fibrosis detection and quantification in whole slide images through deep learning

Davide Panzeri, Elena Pagani, Riccardo Scodellaro, Giuseppe Chirico, Luca Di Tommaso, Donato Inverso, Laura Sironi

Proceedings Volume PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI; PC126220K (2023) https://doi.org/10.1117/12.2673779
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

We present a new AI-based method for the quantification of liver fibrosis in tissue sections stained with Picro Sirius Red which highlights collagen. The method segments and quantifies collagen, a marker of the fibrotic response, through a deep learning model trained on 20 whole-slide images. The results show a Dice score > 90% compared to manual annotations, demonstrating its potential aid during diagnosis. Furthermore, our approach can be extended to other staining protocols.

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Citation Download Citation

Davide Panzeri, Elena Pagani, Riccardo Scodellaro, Giuseppe Chirico, Luca Di Tommaso, Donato Inverso, and Laura Sironi "Fibrosis detection and quantification in whole slide images through deep learning", Proc. SPIE PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, PC126220K (12 August 2023); https://doi.org/10.1117/12.2673779

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KEYWORDS

Image segmentation

Collagen

Deep learning

Liver

RGB color model

Education and training

Matrices

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