A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections

In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.Comment: Accepted for publication in the International Conference on Image Analysis and Recognition (ICIAR 2019

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers

Background: Colour is the most important feature used in quantitative immunohisto- chemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to con rm malignancy. Methods: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was rst trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classi- er is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identi ed using IHC and histochemistry. Results: The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesopha- geal cancer, colon cancer and liver cirrhosis with di erent colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. Conclusions: A robust and e ective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a speci ed colour automatically, is easy to use and avail- able freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html. Testing to the tool by di erent users showed only minor inter-observer variations in results

Whole slide image registration for the study of tumor heterogeneity

Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and charateristics of the tumor microenvironment can be observed and quantified.Comment: MICCAI2018 - Computational Pathology and Ophthalmic Medical Image Analysis - COMPA

Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this researchGarcía-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MÁ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951–961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91–99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063–1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037–1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018Comment: 8 pages, 4 figure

Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts

BACKGROUND: This paper describes a quantitative analysis of the cyst lining architecture in radicular cysts (of inflammatory aetiology) and odontogenic keratocysts (thought to be developmental or neoplastic) including its 2 counterparts: solitary and associated with the Basal Cell Naevus Syndrome (BCNS). METHODS: Epithelial linings from 150 images (from 9 radicular cysts, 13 solitary keratocysts and 8 BCNS keratocysts) were segmented into theoretical cells using a semi-automated partition based on the intensity of the haematoxylin stain which defined exclusive areas relative to each detected nucleus. Various morphometrical parameters were extracted from these "cells" and epithelial layer membership was computed using a systematic clustering routine. RESULTS: Statistically significant differences were observed across the 3 cyst types both at the morphological and architectural levels of the lining. Case-wise discrimination between radicular cysts and keratocyst was highly accurate (with an error of just 3.3%). However, the odontogenic keratocyst subtypes could not be reliably separated into the original classes, achieving discrimination rates slightly above random allocations (60%). CONCLUSION: The methodology presented is able to provide new measures of epithelial architecture and may help to characterise and compare tissue spatial organisation as well as provide useful procedures for automating certain aspects of histopathological diagnosis

Fast automatic quantitative cell replication with fluorescent live cell imaging

Hoffmann N, Keck M, Neuweger H, et al. Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets. BMC Bioinformatics. 2012;13(1): 21.Background Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. Results In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CEMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CEMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). Conclusions We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CEMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net webcite. The evaluation scripts of the present study are available from the same source

Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D

We describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroids to be arranged into a gel-based array directly from a culture plate for downstream processing and analysis. Compared to processing individual samples, the technique uses 11-fold less reagents, saves time and enables automated imaging. To illustrate the power of the technology, we showcase applications of the methodology for investigating 3D spheroid morphology and marker expression and for in vitro safety and efficacy screens. Firstly, spheroid arrays of 11 cell-lines were rapidly assessed for differences in spheroid morphology. Secondly, highly-positive (SOX-2), moderately-positive (Ki-67) and weakly-positive (βIII-tubulin) protein targets were detected and quantified. Third, the arrays enabled screening of ten media compositions for inducing differentiation in human neurospheres. Lastly, the application of spheroid microarrays for spheroid-based drug-screens was demonstrated by quantifying the dose-dependent drop in proliferation and increase in differentiation in etoposide-treated neurospheres

Antiproliferative efficacies but minor drug transporter inducing effects of paclitaxel, cisplatin, or 5-fluorouracil in a murine xenograft model for head and neck squamous cell carcinoma

Drug-induced multidrug resistance (MDR) has been linked to overexpression of drug transporting proteins in head and neck squamous cell carcinoma (HNSCC) in vitro. The aim of this work was to reassess these findings in a murine xenograft model. NOD-SCID mice xenotransplanted with 106 HNO97 cells were treated for four consecutive weeks with weekly paclitaxel, biweekly cisplatin (both intraperitoneal), or 5-fluorouracil (5-FU, administered by osmotic pump). Tumor volume and body weight were weekly documented. Expression of drug transporters and Ki-67 marker were examined using quantitative real-time polymerase chain reaction and/or immunohistochemistry. Both paclitaxel and cisplatin significantly reduced tumor volumes after 2–3 weeks. 5-FU-treated animals had significantly lower body weights after 2 or 4 weeks of chemotherapy. None of the drugs affected expression of drug transporters at the mRNA level. However, P-glycoprotein (Pgp) protein expression was increased by paclitaxel (P < 0.01). Ki-67 expression did not change during treatment irrespective of the drug applied. Paclitaxel and cisplatin are effectively tumor volume reducing drugs in a murine xenograft model of HNSCC. Paclitaxel enhanced Pgp expression at the protein level, but not at the mRNA level suggesting transcriptional induction to be of minor relevance. In contrast, posttranscriptional mechanisms or Darwinian selection of intrinsically drug transporter overexpressing MDR cells might lead to iatrogenic chemotherapy resistance in HNSCC.Fil: Theile, Dirk. Universität Heidelberg; AlemaniaFil: Gal, Zoltan. Universität Heidelberg; AlemaniaFil: Warta, Rolf. Universität Heidelberg; AlemaniaFil: Rigalli, Juan Pablo. Universität Heidelberg; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lahrmann, Bernd. Universität Heidelberg; AlemaniaFil: Grabe, Niels. Universität Heidelberg; AlemaniaFil: Herold Mende, Christel. Universität Heidelberg; AlemaniaFil: Dyckhoff, Gerhard. Universität Heidelberg; AlemaniaFil: Weiss, Johanna. Universität Heidelberg; Alemani

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches