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.
H&E stained sections are the gold standard for disease diagnosis but, unfortunately, the staining process is time-consuming and expensive. In an effort to overcome these problems, here, we propose a virtual staining algorithm, able to predict an Hematoxylin/Eosin (H&E) image, usually exploited during clinical evaluations, starting from the autofluorescence signal of entire liver tissue sections acquired by a confocal microscope. The color and texture contents of the generated virtually stained images have been analyzed through the phasor-based approach to detect tumorous tissue and to segment relevant biological structures (accuracy>90% compared to the expert manual analysis).
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