Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.
School of Software, Hefei University of Technology, Hefei 230601, China.
Comput Methods Programs Biomed. 2019 Mar;170:107-120. doi: 10.1016/j.cmpb.2019.01.008. Epub 2019 Jan 15.
Color consistency of histological images is significant for developing reliable computer-aided diagnosis (CAD) systems. However, the color appearance of digital histological images varies across different specimen preparations, staining, and scanning situations. This variability affects the diagnosis and decreases the accuracy of CAD approaches. It is important and challenging to develop effective color normalization methods for digital histological images.
We proposed a novel adaptive color deconvolution (ACD) algorithm for stain separation and color normalization of hematoxylin-eosin-stained whole slide images (WSIs). To avoid artifacts and reduce the failure rate of normalization, multiple prior knowledges of staining are considered and embedded in the ACD model. To improve the capacity of color normalization for various WSIs, an integrated optimization is designed to simultaneously estimate the parameters of the stain separation and color normalization. The solving of ACD model and application of the proposed method involves only pixel-wise operation, which makes it very efficient and applicable to WSIs.
The proposed method was evaluated on four WSI-datasets including breast, lung and cervix cancers and was compared with 6 state-of-the-art methods. The proposed method achieved the most consistent performance in color normalization according to the quantitative metrics. Through a qualitative assessment for 500 WSIs, the failure rate of normalization was 0.4% and the structure and color artifacts were effectively avoided. Applied to CAD methods, the area under receiver operating characteristic curve for cancer image classification was improved from 0.842 to 0.914. The average time of solving the ACD model is 2.97 s.
The proposed ACD model has prone effective for color normalization of hematoxylin-eosin-stained WSIs in various color appearances. The model is robust and can be applied to WSIs containing different lesions. The proposed model can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.
组织学图像的颜色一致性对于开发可靠的计算机辅助诊断（CAD）系统非常重要。然而，不同的标本制备、染色和扫描情况会导致数字组织学图像的颜色外观发生变化。这种可变性会影响诊断并降低 CAD 方法的准确性。因此，开发有效的数字组织学图像颜色归一化方法非常重要且具有挑战性。
我们提出了一种新颖的自适应颜色去卷积（ACD）算法，用于苏木精-伊红染色全切片图像（WSI）的染色分离和颜色归一化。为了避免伪影并降低归一化失败率，ACD 模型中嵌入了多个染色的先验知识。为了提高对各种 WSI 的颜色归一化能力，设计了一种集成优化方法，以同时估计染色分离和颜色归一化的参数。ACD 模型的求解和所提出方法的应用仅涉及像素级操作，这使其非常高效且适用于 WSI。
该方法在包括乳腺癌、肺癌和宫颈癌在内的四个 WSI 数据集上进行了评估，并与 6 种最先进的方法进行了比较。根据定量指标，所提出的方法在颜色归一化方面表现出最一致的性能。通过对 500 个 WSI 的定性评估，归一化失败率为 0.4%，有效避免了结构和颜色伪影。应用于 CAD 方法时，癌症图像分类的接收者操作特征曲线下面积从 0.842 提高到 0.914。求解 ACD 模型的平均时间为 2.97 秒。
所提出的 ACD 模型对于各种颜色外观的苏木精-伊红染色 WSI 的颜色归一化非常有效。该模型稳健且可应用于包含不同病变的 WSI。所提出的模型可以高效求解，并且可以有效提高癌症图像识别的性能，足以开发基于 WSI 的自动 CAD 程序和系统。
