Variational Bayesian Blind Color Deconvolution of Histopathological Images

2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach.Visual Information Processing (Ref. TIC-116

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

This work was sponsored in part by the Agencia Es-tatal de Investigacion under project PID2019-105142RB-C22/AEI/10.13039/50110 0 011033, Junta de Andalucia under project PY20_00286,and the work by Fernando Perez-Bueno was spon-sored by Ministerio de Economia, Industria y Competitividad un-der FPI contract BES-2017-081584. Funding for open access charge: Universidad de Granada/CBUA.Background and Objective: Color variations in digital histopathology severely impact the performance of computer-aided diagnosis systems. They are due to differences in the staining process and acquisition system, among other reasons. Blind color deconvolution techniques separate multi-stained images into single stained bands which, once normalized, can be used to eliminate these negative color variations and improve the performance of machine learning tasks. Methods: In this work, we decompose the observed RGB image in its hematoxylin and eosin components. We apply Bayesian modeling and inference based on the use of Super Gaussian sparse priors for each stain together with prior closeness to a given reference color-vector matrix. The hematoxylin and eosin components are then used for image normalization and classification of histological images. The proposed framework is tested on stain separation, image normalization, and cancer classification problems. The results are measured using the peak signal to noise ratio, normalized median intensity and the area under ROC curve on five different databases. Results: The obtained results show the superiority of our approach to current state-of-the-art blind color deconvolution techniques. In particular, the fidelity to the tissue improves 1,27 dB in mean PSNR. The normalized median intensity shows a good normalization quality of the proposed approach on the tested datasets. Finally, in cancer classification experiments the area under the ROC curve improves from 0.9491 to 0.9656 and from 0.9279 to 0.9541 on Camelyon-16 and Camelyon-17, respectively, when the original and processed images are used. Furthermore, these figures of merits are better than those obtained by the methods compared with. Conclusions: The proposed framework for blind color deconvolution, normalization and classification of images guarantees fidelity to the tissue structure and can be used both for normalization and classification. In addition, color deconvolution enables the use of the optical density space for classification, which improves the classification performance.Agencia Es-tatal de Investigacion PID2019-105142RB-C22/AEI/10.13039/50110 0 011033Junta de Andalucia PY20_00286Ministerio de Economia, Industria y Competitividad under FPI BES-2017-081584Universidad de Granada/CBU

Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification

This work was supported by project PID2019-105142RB-C22 funded by MCIN / AEI / 10.13039 / 501100011033, Spain, and project P20_00286 funded by FEDER /Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Spain. The work by Fernando Pérez-Bueno was sponsored by Ministerio de Economía, Industria y Competitividad , Spain, under FPI contract BES-2017-081584 . Funding for open access charge: Universidad de Granada / CBUA, Spain.Stain variation between images is a main issue in the analysis of histological images. These color variations, produced by different staining protocols and scanners in each laboratory, hamper the performance of computer-aided diagnosis (CAD) systems that are usually unable to generalize to unseen color distributions. Blind color deconvolution techniques separate multi-stained images into single stained bands that can then be used to reduce the generalization error of CAD systems through stain color normalization and/or stain color augmentation. In this work, we present a Bayesian modeling and inference blind color deconvolution framework based on the K-Singular Value Decomposition algorithm. Two possible inference procedures, variational and empirical Bayes are presented. Both provide the automatic estimation of the stain color matrix, stain concentrations and all model parameters. The proposed framework is tested on stain separation, image normalization, stain color augmentation, and classification problems.CBUAJunta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y UniversidadesFamily Process Institute BES-2017-081584Universidad de GranadaEuropean Regional Development FundMinisterio de Economía, Industria y Competitividad, Gobierno de EspañaAgencia Estatal de Investigación P20_0028

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color preprocessing techniques are frequently mentioned in the literature, while other areas are usually ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.European Commission 860627Ministerio de Ciencia e Innovacion (MCIN)/Agencia Estatal de Investigacion (AEI) PID2019-105142RB-C22Fondo Europeo de Desarrollo Regional (FEDER)/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades B-TIC-324-UGR20Instituto de Salud Carlos III Spanish Government European Commission BES-2017-08158

Stain separation in digital bright field histopathology

Digital pathology employs images that were acquired by imaging thin tissue samples through a microscope. The preparation of a sample from a biopt to the glass slide entering the imaging device is done manually introducing large variability in the samples to be imaged. For visible contrast it is necessary to stain the samples prior to imaging. Different stains attach to different compounds elucidating the different cellular structures. Towards automatic analysis and for visual comparability there is a need to standardize the images to obtain consistent appearances regardless of the potential differences in sample preparation. A standard approach is to unmix the the various stains computationally, normalize each separate stain image and to recombine these. This paper describes a modification to a standard blind method for stain normalization. The performance is quantified in terms of annotated expert data. Theoretical analysis is presented to rationalize the new approach

Stain separation in digital bright field histopathology

Digital pathology employs images that were acquired by imaging thin tissue samples through a microscope. The preparation of a sample from a biopt to the glass slide entering the imaging device is done manually introducing large variability in the samples to be imaged. For visible contrast it is necessary to stain the samples prior to imaging. Different stains attach to different compounds elucidating the different cellular structures. Towards automatic analysis and for visual comparability there is a need to standardize the images to obtain consistent appearances regardless of the potential differences in sample preparation. A standard approach is to unmix the the various stains computationally, normalize each separate stain image and to recombine these. This paper describes a modification to a standard blind method for stain normalization. The performance is quantified in terms of annotated expert data. Theoretical analysis is presented to rationalize the new approach