SCAN algorithm dataset

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.

本代码仓库包含下述研究中使用的图像数据集与人工标注数据：
- Salvi M.、Michielli N.与Molinari F.，《染色颜色自适应归一化（Stain Color Adaptive Normalization，简称SCAN）：数字病理中组织染色的分离与标准化》，《Computer Methods and Programs in Biomedicine》2020年（DOI: 10.1016/j.cmpb.2020.105506）

摘要
组织病理图像的诊断依赖于光学显微镜下组织切片的视觉分析。然而，染色流程、操作人员技术水平及扫描仪参数的差异，会导致组织样本呈现出不同的色彩强度。这种染色变异性会干扰病理医师的诊断，并降低计算机辅助诊断系统的准确率。在此背景下，染色归一化流程已被证实为解决该问题的有效手段，可实现源图像与参考图像之间的染色色彩外观标准化。
本文提出了针对苏木精-伊红（hematoxylin and eosin，简称HE）染色组织切片的全新全自动染色分离与归一化方法。所提出的算法命名为SCAN（Stain Color Adaptive Normalization，染色颜色自适应归一化），其基于细胞结构检测的分割与聚类策略。该算法可提升病理组织与背景之间的对比度，并在不改变管腔与背景色彩的前提下保留局部组织结构。
本研究在多组织、多尺度数据集上对染色分离与归一化技术进行了定性与定量验证，结果令人满意，其性能优于当前前沿方法。此外，SCAN在全切片图像上的测试同样表现优异，且计算耗时较低。
所提出的标准化方法具有两方面潜在价值：一是可优化数字病理领域的视觉诊断流程，二是可为癌症检测的自动化分类技术提供高效的预处理策略。