Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

Unsupervised morphological segmentation of tissue compartments in histopathological images

Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary ‘virtual-cells’, each enclosing a potential ‘nucleus’ (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms.

Automated Classification of Breast Cancer Stroma Maturity From Histological Images

OBJECTIVE

The tumor microenvironment plays a crucial role in regulating tumor progression by a number of different mechanisms, in particular, the remodeling of collagen fibers in tumor-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodeling of collagen fibers gives rise to observable patterns in hematoxylin and eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies.

METHODS

We use multiscale basic image features and local binary patterns, in combination with a random decision trees classifier for classification of breast cancer stroma regions-of-interest (ROI).

RESULTS

We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%.

CONCLUSION

This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E-stained slides at least as well as skilled observers.

Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM

BACKGROUND AND OBJECTIVE

Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies.

METHOD

In this paper, we demonstrate that it is possible to automatically classify cardiovascular tissues using texture information and Support Vector Machines (SVM). Additionally, we realised that it is feasible to recognise several cardiovascular organs following the same process. The texture of histological images was described using Local Binary Patterns (LBP), LBP Rotation Invariant (LBPri), Haralick features and different concatenations between them, representing in this way its content. Using a SVM with linear kernel, we selected the more appropriate descriptor that, for this problem, was a concatenation of LBP and LBPri. Due to the small number of the images available, we could not follow an approach based on deep learning, but we selected the classifier who yielded the higher performance by comparing SVM with Random Forest and Linear Discriminant Analysis. Once SVM was selected as the classifier with a higher area under the curve that represents both higher recall and precision, we tuned it evaluating different kernels, finding that a linear SVM allowed us to accurately separate four classes of tissues: (i) cardiac muscle of the heart, (ii) smooth muscle of the muscular artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation was conducted using 3000 blocks of 100 × 100 sized pixels, with 600 blocks per class and the classification was assessed using a 10-fold cross-validation.

RESULTS

using LBP as the descriptor, concatenated with LBPri and a SVM with linear kernel, the main four classes of tissues were recognised with an AUC higher than 0.98. A polynomial kernel was then used to separate the elastic artery and vein, yielding an AUC in both cases superior to 0.98.

CONCLUSION

Following the proposed approach, it is possible to separate with very high precision (AUC greater than 0.98) the fundamental tissues of the cardiovascular system along with some organs, such as the heart, arteries and veins.

A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues

Organotypic, three-dimensional (3D) cancer models have enabled investigations of complex microtissues in increasingly realistic conditions. However, a drawback of these advanced models remains the poor biological relevance of cancer cell lines, while higher clinical significance would be obtainable with patient-derived cell cultures. Here, we describe the generation and data analysis of 3D microtissue models from patient-derived xenografts (PDX) of non-small cell lung carcinoma (NSCLC). Standard of care anti-cancer drugs were applied and the altered multicellular morphologies were captured by confocal microscopy, followed by automated image analyses to quantitatively measure phenotypic features for high-content chemosensitivity tests. The obtained image data were thresholded using a local entropy filter after which the image foreground was split into local regions, for a supervised classification into tumor or fibroblast cell types. Robust statistical methods were applied to evaluate treatment effects on growth and morphology. Both novel and existing computational approaches were compared at each step, while prioritizing high experimental throughput. Docetaxel was found to be the most effective drug that blocked both tumor growth and invasion. These effects were also validated in PDX tumors in vivo. Our research opens new avenues for high-content drug screening based on patient-derived cell cultures, and for personalized chemosensitivity testing.

Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology

The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical [Formula: see text]-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of [Formula: see text] and accuracy of 0.92, on [Formula: see text] glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-time diagnoses and interventions for renal care.

New computerized staging method to analyze mink testicular tissue in environmental research

Histopathology of testicular tissue is considered to be the most sensitive tool to detect adverse effects on male reproduction. When assessing tissue damage, seminiferous epithelium needs to be classified into different stages to detect certain cell damages; but stage identification is a demanding task. The authors present a method to identify the 12 stages in mink testicular tissue. The staging system uses Gata-4 immunohistochemistry to visualize acrosome development and proved to be both intraobserver-reproducible and interobserver-reproducible with a substantial agreement of 83.6% (kappa = 0.81) and 70.5% (kappa = 0.67), respectively. To further advance and objectify this method, they present a computerized staging system that identifies these 12 stages. This program has an agreement of 52.8% (kappa 0.47) with the consensus staging by 2 investigators. The authors propose a pooling of the stages into 5 groups based on morphology, stage transition, and toxicologically important endpoints. The computerized program then reached a substantial agreement of 76.7% (kappa = 0.69). The computerized staging tool uses local ternary patterns to describe the texture of the tubules and a support vector machine classifier to learn which textures correspond to which stages. The results have the potential to modernize the tedious staging process required in toxicological evaluation of testicular tissue, especially if combined with whole-slide imaging and automated tubular segmentation. Environ Toxicol Chem 2017;36:156-164. © 2016 The Authors. Environmental Toxicology and Chemistry Published by Wiley Periodicals, Inc. on behalf of SETAC.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

BACKGROUND

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples.

METHODS

Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists.

RESULTS

In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78).

CONCLUSIONS

Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the "gas of near circles" active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.

Image analysis and machine learning in digital pathology: Challenges and opportunities

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.

Multi-class texture analysis in colorectal cancer histology

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach

PURPOSE

Pathologists often look at whole slide images (WSIs) at low magnification to find potentially important regions and then zoom in to higher magnification to perform more sophisticated analysis of the tissue structures. Many automated methods of WSI analysis attempt to preprocess the down-sampled image in order to select salient regions which are then further analyzed by a more computationally intensive step at full magnification. Although it can greatly reduce processing times, this process may lead to small potentially important regions being overlooked at low magnification. We propose a texture analysis technique to ease the processing of H&E stained WSIs by triaging clinically important regions.

METHOD

Image patches randomly selected from the whole tissue area were divided into smaller tiles and Gaussian-like texture filters were applied to them. Texture filter responses from each tile were combined together and statistical measures were derived from their histograms of responses. Bag of visual words pipeline was then employed to combine extracted features from tiles to form one histogram of words per every image patch. A support vector machine classifier was trained using the calculated histograms of words to be able to distinguish between clinically relevant and irrelevant patches.

RESULT

Experimental analysis on 5151 image patches from 10 patient cases (65 tissue slides) indicated that our proposed texture technique out-performed two previously proposed colour and intensity based methods with an area under the ROC curve of 0.87.

CONCLUSION

Texture features can be employed to triage clinically important areas within large WSIs.

Influence of Texture and Colour in Breast TMA Classification

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n=19) and test (n=191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN+PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J=59.5 ± 14.6 and Rand Ri=62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN=35.2 ± 24.9, OBN=49.6 ± 32, JPCa=49.5 ± 18.5, OPCa=72.7 ± 14.8 and Ri=60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.

Prognostic significance of human tissue kallikrein-related peptidases 11 and 15 in gastric cancer

Human tissue kallikrein-related peptidases (KLK) are a group of 15 serine proteases which have been investigated as potential cancer biomarkers. This study determined the prognostic significance of KLK 11 and 15 expression levels in gastric carcinoma specimens. Expression of KLK11 and KLK15 was assessed by immunohistochemistry staining on a tissue microarray constructed from 113 gastrectomy specimens from patients with gastric carcinoma. To minimize inter-observer variability, expression levels were quantified using an automated algorithm. Epithelial and stromal staining were assessed separately. Both KLK11 and KLK15 were expressed in gastric carcinoma. There was no significant correlation between either KLK11 or KLK15 expression and the presence of lymph node metastases or Lauren classification (intestinal vs. diffuse). Higher levels of KLK11 expression in gastric carcinoma were associated with significantly worse overall survival (p = 0.008), and a multivariate analysis showed that it had prognostic value independent of tumor stage and differentiation (p = 0.004). Variations in KLK15 expression were not significantly associated with prognosis. KLK11 shows promise as a potential independent prognostic marker for gastric carcinoma.

Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis

Aims

To build and evaluate an automated method for assessing tumour viability in histological tissue samples using texture features and supervised learning.

Methods

H&E-stained sections (n=56) of human non-small cell lung adenocarcinoma xenografts were digitised with a whole-slide scanner. A novel image analysis method based on local binary patterns and a support vector machine classifier was trained with a set of sample regions (n=177) extracted from the whole-slide images and tested with another set of images (n=494). The extracted regions, or single-tissue entity images, were chosen to represent as pure as possible examples of three morphological tissue entities: viable tumour tissue, non-viable tumour tissue and mouse host tissue.

Results

An agreement of 94.5% (area under the curve=0.995, kappa=0.90) was achieved to classify the single-tissue entity images in the test set (n=494) into the viable tumour and non-viable tumour tissue categories. The algorithm assigned 250 of the 252 non-viable and 219 of the 242 of viable sample regions to the correct categories, respectively. This corresponds to a sensitivity of 90.5% and specificity of 99.2%.

Conclusions

The proposed image analysis-based tumour viability assessment resulted in a high agreement with expert annotations. By providing extraction of detailed information of the tumour microenvironment, the automated method can be used in preclinical research settings. The method could also have implications in cancer diagnostics, cancer outcome prognostics and prediction.

Breast cancer histopathology image analysis: a review

This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

Magnetic resonance image tissue classification using an automatic method

Background

Brain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex).

Methods

In this paper we present a simple and more accurate automated technique for brain segmentation into White Matter, Gray Matter and Cerebrospinal fluid (CSF) in three-dimensional MR images. The algorithm’s three steps are histogram based segmentation, feature extraction and final classification using SVM. The integrated algorithm has more accurate results than what can be obtained with its individual components. To produce much more efficient segmentation method our framework captures different types of features in each step that are of special importance for MRI, i.e., distributions of tissue intensities, textural features, and relationship with neighboring voxels or spatial features.

Results

Our method has been validated on real images and simulated data, with desirable performance in the presence of noise and intensity inhomogeneities.

Conclusions

The experimental results demonstrate that our proposed method is a simple and accurate technique to define brain tissues with high reproducibility in comparison with other techniques.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_207

Elevated CXCL1 expression in breast cancer stroma predicts poor prognosis and is inversely associated with expression of TGF-β signaling proteins

Background

CXCL1 is a chemotactic cytokine shown to regulate breast cancer progression and chemo-resistance. However, the prognostic significance of CXCL1 expression in breast cancer has not been fully characterized. Fibroblasts are important cellular components of the breast tumor microenvironment, and recent studies indicate that this cell type is a potential source of CXCL1 expression in breast tumors. The goal of this study was to further characterize the expression patterns of CXCL1 in breast cancer stroma, determine the prognostic significance of stromal CXCL1 expression, and identify factors affecting stromal CXCL1 expression.

Methods

Stromal CXCL1 protein expression was analyzed in 54 normal and 83 breast carcinomas by immunohistochemistry staining. RNA expression of CXCL1 in breast cancer stroma was analyzed through data mining in http://www.Oncomine.org. The relationships between CXCL1 expression and prognostic factors were analyzed by univariate analysis. Co-immunofluorescence staining for CXCL1, α-Smooth Muscle Actin (α-SMA) and Fibroblast Specific Protein 1 (FSP1) expression was performed to analyze expression of CXCL1 in fibroblasts. By candidate profiling, the TGF-β signaling pathway was identified as a regulator of CXCL1 expression in fibroblasts. Expression of TGF-β and SMAD gene products were analyzed by immunohistochemistry and data mining analysis. The relationships between stromal CXCL1 and TGF-β signaling components were analyzed by univariate analysis. Carcinoma associated fibroblasts isolated from MMTV-PyVmT mammary tumors were treated with recombinant TGF-β and analyzed for CXCL1 promoter activity by luciferase assay, and protein secretion by ELISA.

Results

Elevated CXCL1 expression in breast cancer stroma correlated with tumor grade, disease recurrence and decreased patient survival. By co-immunofluorescence staining, CXCL1 expression overlapped with expression of α-SMA and FSP1 proteins. Expression of stromal CXCL1 protein expression inversely correlated with expression of TGF-β signaling components. Treatment of fibroblasts with TGF-β suppressed CXCL1 secretion and promoter activity.

Conclusions

Increased CXCL1 expression in breast cancer stroma correlates with poor patient prognosis. Furthermore, CXCL1 expression is localized to α-SMA and FSP1 positive fibroblasts, and is negatively regulated by TGF-β signaling. These studies indicate that decreased TGF-β signaling in carcinoma associated fibroblasts enhances CXCL1 expression in fibroblasts, which could contribute to breast cancer progression.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-781) contains supplementary material, which is available to authorized users.

A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears

Introduction

Microscopy is the gold standard for diagnosis of malaria, however, manual evaluation of blood films is highly dependent on skilled personnel in a time-consuming, error-prone and repetitive process. In this study we propose a method using computer vision detection and visualization of only the diagnostically most relevant sample regions in digitized blood smears.

Methods

Giemsa-stained thin blood films with P. falciparum ring-stage trophozoites (n = 27) and uninfected controls (n = 20) were digitally scanned with an oil immersion objective (0.1 µm/pixel) to capture approximately 50,000 erythrocytes per sample. Parasite candidate regions were identified based on color and object size, followed by extraction of image features (local binary patterns, local contrast and Scale-invariant feature transform descriptors) used as input to a support vector machine classifier. The classifier was trained on digital slides from ten patients and validated on six samples.

Results

The diagnostic accuracy was tested on 31 samples (19 infected and 12 controls). From each digitized area of a blood smear, a panel with the 128 most probable parasite candidate regions was generated. Two expert microscopists were asked to visually inspect the panel on a tablet computer and to judge whether the patient was infected with P. falciparum. The method achieved a diagnostic sensitivity and specificity of 95% and 100% as well as 90% and 100% for the two readers respectively using the diagnostic tool. Parasitemia was separately calculated by the automated system and the correlation coefficient between manual and automated parasitemia counts was 0.97.

Conclusion

We developed a decision support system for detecting malaria parasites using a computer vision algorithm combined with visualization of sample areas with the highest probability of malaria infection. The system provides a novel method for blood smear screening with a significantly reduced need for visual examination and has a potential to increase the throughput in malaria diagnostics.

Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis

Computer-aided diagnosis (CAD) systems in gray-scale breast ultrasound images have the potential to reduce unnecessary biopsy of breast masses. The purpose of our study is to develop a robust CAD system based on the texture analysis. First, gray-scale invariant features are extracted from ultrasound images via multi-resolution ranklet transform. Thus, one can apply linear support vector machines (SVMs) on the resulting gray-level co-occurrence matrix (GLCM)-based texture features for discriminating the benign and malignant masses. To verify the effectiveness and robustness of the proposed texture analysis, breast ultrasound images obtained from three different platforms are evaluated based on cross-platform training/testing and leave-one-out cross-validation (LOO-CV) schemes. We compare our proposed features with those extracted by wavelet transform in terms of receiver operating characteristic (ROC) analysis. The AUC values derived from the area under the curve for the three databases via ranklet transform are 0.918 (95% confidence interval [CI], 0.848 to 0.961), 0.943 (95% CI, 0.906 to 0.968), and 0.934 (95% CI, 0.883 to 0.961), respectively, while those via wavelet transform are 0.847 (95% CI, 0.762 to 0.910), 0.922 (95% CI, 0.878 to 0.958), and 0.867 (95% CI, 0.798 to 0.914), respectively. Experiments with cross-platform training/testing scheme between each database reveal that the diagnostic performance of our texture analysis using ranklet transform is less sensitive to the sonographic ultrasound platforms. Also, we adopt several co-occurrence statistics in terms of quantization levels and orientations (i.e., descriptor settings) for computing the co-occurrence matrices with 0.632+ bootstrap estimators to verify the use of the proposed texture analysis. These experiments suggest that the texture analysis using multi-resolution gray-scale invariant features via ranklet transform is useful for designing a robust CAD system.

Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images

An automated computer-aided diagnosis system is developed to classify benign and malignant thyroid nodules using multi-stained fine needle aspiration biopsy (FNAB) cytological images. In the first phase, the image segmentation is performed to remove the background staining information and retain the appropriate foreground cell objects in cytological images using mathematical morphology and watershed transform segmentation methods. Subsequently, statistical features are extracted using two-level discrete wavelet transform (DWT) decomposition, gray level co-occurrence matrix (GLCM) and Gabor filter based methods. The classifiers k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) are tested for classifying benign and malignant thyroid nodules. The combination of watershed segmentation, GLCM features and k-NN classifier results a lowest diagnostic accuracy of 60 %. The highest diagnostic accuracy of 93.33 % is achieved by ENN classifier trained with the statistical features extracted by Gabor filter bank from the images segmented by morphology and watershed transform segmentation methods. It is also observed that SVM classifier results its highest diagnostic accuracy of 90 % for DWT and Gabor filter based features along with morphology and watershed transform segmentation methods. The experimental results suggest that the developed system with multi-stained thyroid FNAB images would be useful for identifying thyroid cancer irrespective of staining protocol used.

Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions

INTRODUCTION

The notion of local scale was introduced to characterize varying levels of image detail so that localized image processing tasks could be performed while simultaneously yielding a globally optimal result. In this paper, we have presented the methodological framework for a novel locally adaptive scale definition, morphologic scale (MS), which is different from extant local scale definitions in that it attempts to characterize local heterogeneity as opposed to local homogeneity.

METHODS

At every point of interest, the MS is determined as a series of radial paths extending outward in the direction of least resistance, navigating around obstructions. Each pixel can then be directly compared to other points of interest via a rotationally invariant quantitative feature descriptor, determined by the application of Fourier descriptors to the collection of these paths.

RESULTS

OUR GOAL IS TO DISTINGUISH TUMOR AND STROMAL TISSUE CLASSES IN THE CONTEXT OF FOUR DIFFERENT DIGITIZED PATHOLOGY DATASETS: prostate tissue microarrays (TMAs) stained with hematoxylin and eosin (HE) (44 images) and TMAs stained with only hematoxylin (H) (44 images), slide mounts of ovarian H (60 images), and HE breast cancer (51 images) histology images. Classification performance over 50 cross-validation runs using a Bayesian classifier produced mean areas under the curve of 0.88 ± 0.01 (prostate HE), 0.87 ± 0.02 (prostate H), 0.88 ± 0.01 (ovarian H), and 0.80 ± 0.01 (breast HE).

CONCLUSION

For each dataset listed in Table 3, we randomly selected 100 points per image, and using the procedure described in Experiment 1, we attempted to separate them as belonging to stroma or epithelium.

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

Background

Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.

Methods

Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.

Results

Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.

Conclusions

The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995