SCAN algorithm dataset

Published: 17-04-2020| Version 1 | DOI: 10.17632/sc878z8pm3.1
Contributors:
Massimo Salvi,
Nicola Michielli,
Filippo Molinari

Description

This repository contains the image dataset and the manual annotations used in the following work: - Salvi M., Michielli N., and Molinari F., "Stain Color Adaptive Normalization (SCAN): separation and standardization of histological stains in digital pathology", Computer Methods and Programs in Biomedicine 2020 (DOI: 10.1016/j.cmpb.2020.105506) ABSTRACT The diagnosis of histopathological images is based on the visual analysis of tissue slices under a light microscope. However, the histological tissue appearance may assume different color intensities depending on the staining process, operator ability and scanner specifications. This stain variability affects the diagnosis of the pathologist and decreases the accuracy of computer-aided diagnosis systems. In this context, the stain normalization process has proved to be a powerful tool to cope with this issue, allowing to standardize the stain color appearance of a source image respect to a reference image. In this paper, novel fully automated stain separation and normalization approaches for hematoxylin and eosin stained histological slides are presented. The proposed algorithm, named SCAN (Stain Color Adaptive Normalization), is based on segmentation and clustering strategies for cellular structures detection. The SCAN algorithm is able to improve the contrast between histological tissue and background and preserve local structures without changing the color of the lumen and the background. Both stain separation and normalization techniques were qualitatively and quantitively validated on a multi-tissue and multiscale dataset, with highly satisfactory results, outperforming the state-of-the-art approaches. SCAN was also tested on whole-slide images with high performances and low computational times. The potential contribution of the proposed standardization approach is twofold: the improvement of visual diagnosis in digital histopathology and the development of powerful pre-processing strategies to automated classification techniques for cancer detection.

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