Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides

Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localised scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Methods and Results: Regions of tumour in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883 respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. Conclusions: This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration

Stain deconvolution using statistical analysis of multi-resolution stain colour representation

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners

A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

In many research laboratories, it is essential to determine the relative expression levels of some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher and assigning a score to each image that should represent some predefined characteristic of the IHC staining, such as its intensity. However, manual scoring depends on the judgment of an observer and therefore exposes the assessment to a certain level of bias. In this work, we present a fully automatic and unsupervised method for comparative biomarker quantification in histopathological brightfield images. The method relies on a color separation method that discriminates between two chromogens expressed as brown and blue colors robustly, independent of color variation or biomarker expression level. For this purpose, we have adopted a two-stage stain separation approach in the optical density space. First, a preliminary separation is performed using a deconvolution method in which the color vectors of the stains are determined after an eigendecomposition of the data. Then, we adjust the separation using the non-negative matrix factorization method with beta divergences, initializing the algorithm with the matrices resulting from the previous step. After that, a feature vector of each image based on the intensity of the two chromogens is determined. Finally, the images are annotated using a systematically initialized k-means clustering algorithm with beta divergences. The method clearly defines the initial boundaries of the categories, although some flexibility is added. Experiments for the semi-quantitative scoring of images in five categories have been carried out by comparing the results with the scores of four expert researchers yielding accuracies that range between 76.60% and 94.58%. These results show that the proposed automatic scoring system, which is definable and reproducible, produces consistent results.FEDER / Junta de Andalucía-Consejería de Economía y Conocimiento US-1264994Fondo de Desarrollo (FEDER). Unión Europea PGC2018-096244-B-I00, SAF2016-75442-RMinisterio de Economía, Industria y Competitividad (MINECO). España TEC2017- 82807-

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low-and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach

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

Automated characterization of Tumor-Infiltrating Lymphocytes (TIL) in histological breast images

Cancer illness has a big influence on society. Its extended proliferation and high aggressiveness make it a difficult problem to solve and therefore a big deal for science. Recently, a research trend has been focusing on how 3D tumor structure affects the development of the cancer and its outcome, especially metastasis. Stromal structure and tumor cell signaling are processes that highly influence tumor migration. Thus, histological analysis becomes a fundamental tool to study tumor structure, which provides valuable information about cell characteristics and organization. The relevance of histological study is supported by the increasing interest of anatomopathologists to have good automatic solutions to support the specialist’s diagnosis. For this purpose, the current thesis proposes an automated approach to analyze hematoxylin and eosin (H&E) stained histological images, particularly coming from breast cancer patients. The proposed method consists on the classification of the nuclei in H&E-stained histological images and the further analysis of tumor-infiltrating lymphocytes (TIL) present on the visualized section. The starting point of the approach is the automatic nuclei-segmented binary mask. Each of the segmented nuclei is classified into two types, cancerous or healthy. The classification is performed by a trained artificial neural network to give two binary masks, each of them containing one type of nuclei. Then, the algorithm can follow two different paths: classification of zones or TIL analysis. Classification of zones has the aim to provide a more comfortable support to perform cancer diagnosis, because it provides quantitative information of tumor lobule size. To achieve it, a nuclei correction step is executed, by which each nucleus class depends on the area surrounding it. In this way, a clearer vision of the existing zones is provided (tumor lobule or tumor microenvironment). The other approach is to perform TIL analysis. This technique is based on the nuclei classified binary masks and analyzes the immune system response against the tumor. This way, healthy cells of tumor microenvironment are detected and quantified. The ratio of TIL occupied area to free microenvironment area is computed as informational parameter. This ratio is calculated by the combination of a manually-segmented zone binary mask and the nuclei classified binary mask. In this way, only healthy nuclei of microenvironment zone are considered, dividing the sum of their area by the free sections of the microenvironment zone (i.e. area of microenvironment zone where nuclei are not present). Moreover, the TIL dispersion factor is computed to study their distribution throughout the area by dividing the microenvironment area in several zones and calculate the standard deviation of the area of lymphocytes within each of them. Afterward, the opposed of standard deviation is computed to obtain the dispersion factor. Automatic results are found to match the gold standard (the pathologist’s diagnosis), although some error is observed after evaluation. The approach taken in this work has a positive outlook, even though some aspects need to be polished, like the algorithm accuracy and the use of a larger set of images to claim a proper functionality for global cases.Ingeniería Biomédic

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Background: In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension. Software: The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density. Conclusions: Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing