Loading…

Adaptive color deconvolution for histological WSI normalization

•A novel color normalization method for H&E-stained WSIs is proposed.•The color appearance of the normalization is very consistent.•Both structure and color artifacts can be effectively avoided.•The method only involves pixel-wise operation and can be solved in 3 second. Color consistency of his...

Full description

Saved in:
Bibliographic Details
Published in:Computer methods and programs in biomedicine 2019-03, Vol.170, p.107-120
Main Authors: Zheng, Yushan, Jiang, Zhiguo, Zhang, Haopeng, Xie, Fengying, Shi, Jun, Xue, Chenghai
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A novel color normalization method for H&E-stained WSIs is proposed.•The color appearance of the normalization is very consistent.•Both structure and color artifacts can be effectively avoided.•The method only involves pixel-wise operation and can be solved in 3 second. 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.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2019.01.008