Enabling Collagen Quantification on HE-stained Slides Through Stain Deconvolution and Restained HE-HES

TL;DR

This study presents a deep learning method to quantify collagen in HE-stained slides by predicting Saffron stain density, enabling digital creation of HES images without additional staining, thus improving clinical workflow and reducing costs.

Contribution

The paper introduces a UNet-based model to estimate Saffron content from HE images, allowing collagen quantification without extra staining procedures.

Findings

Achieved a Mean Absolute Error of 0.0668 in Saffron density prediction.

Created a dataset of registered HE-HES slides for training and validation.

Demonstrated potential to improve clinical workflows and reduce reagent costs.

Abstract

In histology, the presence of collagen in the extra-cellular matrix has both diagnostic and prognostic value for cancer malignancy, and can be highlighted by adding Saffron (S) to a routine Hematoxylin and Eosin (HE) staining. However, Saffron is not usually added because of the additional cost and because pathologists are accustomed to HE, with the exception of France-based laboratories. In this paper, we show that it is possible to quantify the collagen content from the HE image alone and to digitally create an HES image. To do so, we trained a UNet to predict the Saffron densities from HE images. We created a dataset of registered, restained HE-HES slides and we extracted the Saffron concentrations as ground truth using stain deconvolution on the HES images. Our model reached a Mean Absolute Error of 0.0668 $\pm$ 0.0002 (Saffron values between 0 and 1) on a 3-fold testing set. We hope our approach can aid in improving the clinical workflow while reducing reagent costs for laboratories.