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.
Panzeri, D., Pagani, E., Scodellaro, R., Chirico, G., Di Tommaso, L., Inverso, D., et al. (2023). Fibrosis detection and quantification in whole slide images through deep learning. In Proceedings Volume PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI [10.1117/12.2673779].
Fibrosis detection and quantification in whole slide images through deep learning
Panzeri, D;Pagani, E;Scodellaro, R;Chirico, G;Sironi, L
2023
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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