527 research outputs found

    Context-based Normalization of Histological Stains using Deep Convolutional Features

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    While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.Comment: In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017

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

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    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

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    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

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

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    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
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