Akira Saito

Color correction for automatic fibrosis quantification in liver biopsy specimens

Context: For a precise and objective quantification of liver fibrosis, quantitative evaluations through image analysis have been utilized. However, manual operations are required in most cases for extracting fiber areas because of color variation included in digital pathology images. Aims: The purpose of this research is to propose a color correction method for whole slide images (WSIs) of Elastica van Gieson (EVG) stained liver biopsy tissue specimens and to realize automated operation of image analysis for fibrosis quantification. Materials and Methods: Our experimental dataset consisted of 38 WSIs of liver biopsy specimens collected from 38 chronic viral hepatitis patients from multiple medical facilities, stained with EVG and scanned at ×20 using a Nano Zoomer 2.0 HT (Hamamatsu Photonics K.K., Hamamatsu, Japan). Color correction was performed by modifying the color distribution of a target WSI so as to fit to the reference, where the color distribution was modeled by a set of two triangle pyramids. Using color corrected WSIs; fibrosis quantification was performed based on tissue classification analysis. Statistical Analysis Used: Spearman′s rank correlation coefficients were calculated between liver stiffness measured by transient elastography and median area ratio of collagen fibers calculated based on tissue classification results. Results: Statistical analysis results showed a significant correlation r = 0.61-0.68 even when tissue classifiers were trained by using a subset of WSIs, while the correlation coefficients were reduced to r = 0.40-0.50 without color correction. Conclusions: Fibrosis quantification accompanied with the proposed color correction method could provide an objective evaluation tool for liver fibrosis, which complements semi-quantitative histologic evaluation systems.

Automated gastric cancer diagnosis on HE ltraining a classifier on a large scale with multiple instance machine learning

We present a system that detects cancer on slides of gastric tissue sections stained with hematoxylin and eosin (H&E). At its heart is a classifier trained using the semi-supervised multi-instance learning framework (MIL) where each tissue is represented by a set of regions-of-interest (ROI) and a single label. Such labels are readily obtained because pathologists diagnose each tissue independently as part of the normal clinical workflow. From a large dataset of over 26K gastric tissue sections from over 12K patients obtained from a clinical load spanning several months, we train a MIL classifier on a patient-level partition of the dataset (2/3 of the patients) and obtain a very high performance of 96% (AUC), tested on the remaining 1/3 never-seen before patients (over 8K tissues). We show this level of performance to match the more costly supervised approach where individual ROIs need to be labeled manually. The large amount of data used to train this system gives us confidence in its robustness and that it can be safely used in a clinical setting. We demonstrate how it can improve the clinical workflow when used for pre-screening or quality control. For pre-screening, the system can diagnose 47% of the tissues with a very low likelihood (< 1%) of missing cancers, thus halving the clinicians caseload. For quality control, compared to random rechecking of 33% of the cases, the system achieves a three-fold increase in the likelihood of catching cancers missed by pathologists. The system is currently in regular use at independent pathology labs in Japan where it is used to double-check clinicians diagnoses. At the end of 2012 it will have analyzed over 80,000 slides of gastric and colorectal samples (200,000 tissues).

Characterization of chromatin texture by contour complexity for cancer cell classification

The purpose of this study is to investigate a new technique for image-based cancer cell classification and provide a more quantitative and objective characterization method for a diagnosis, which currently relies on qualitative and empirical judgment of pathologists. For this, a new method for chromatin texture characterization employing a new feature, contour complexity, is proposed and evaluated using nuclear images obtained from paraffin-wax embedded sections of human breast cancer on slides. The proposed feature is calculated on the basis of a contour length of nucleus obtained by setting different threshold values of intensity for a grayscale image, and it is a quantitative measure of chromatin texture. An expectation-maximization (EM) algorithm-based segmentation and an effective initial parameter search method for EM are used for the automatic calculation of the feature. The results for breast cancer cell detection showed that the average contour complexity value for malignant cells (19.6plusmn4.1) is found to be significantly greater (p < 10-6, Kolmogorov-Smirnov test) than that of benign cells (0.35plusmn0.17). By the comparison with the conventional fractal dimension approach, it is shown that the proposed feature is much more sensitive feature than the fractal dimension for the individual cancer cell detection.

Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

Dioxin interferes in chromosomal positioning through the aryl hydrocarbon receptor

Each chromosome occupies its own-specific space called a territory within the interphase nucleus, and the arrangement of chromosome territories (CTs) is important in epigenetic mechanisms. The molecular mechanism to determine the positioning of CTs, however, remains unknown. On the other hand, dioxin is known to be the typical environmental pollutant that affects a wide variety of biological events in many species. Here, we show that dioxin enlarges the minimum distance between chromosome 12 and chromosome 16 territories in human preadipocyte cells, and the alteration of chromosome positioning is canceled by an aryl hydrocarbon receptor (AhR) antagonist alpha-naphthoflavone. Thus, AhR may be a key molecule to regulate chromosome positioning. Our results suggest a novel effect of dioxin toxicity, and demonstrate a clue to reveal the novel molecular mechanism for the arrangement of CTs.

Dawn of the digital diagnosis assisting system, can it open a new age for pathology?

Digital pathology is developing based on the improvement and popularization of WSI (whole slide imaging) scanners. WSI scanners are widely expected to be used as the next generation microscope for diagnosis; however, their usage is currently mostly limited to education and archiving. Indeed, there are still many hindrances in using WSI scanners for diagnosis (not research purpose), two of the main reasons being the perceived high cost and small gain in productivity obtained by switching from the microscope to a WSI system and the lack of WSI standardization. We believe that a key factor for advancing digital pathology is the creation of computer assisted diagnosis systems (CAD). Such systems require high-resolution digitization of slides and provide a clear added value to the often costly conversion to WSI. We (NEC Corporation) are creating a CAD system, named e-Pathologist ®. This system is currently used at independent pathology labs for quality control (QC/QA), double-checking pathologists diagnosis and preventing missed cancers. At the end of 2012, about 80,000 slides, 200,000 tissues of gastric and colorectal samples will have been analyzed by e-Pathologist ®. Through the development of e-Pathologist ®, it has become clear that a computer program should be inspired by the pathologist diagnosis process, yet it should not be a mere copy or simulation of it. Indeed pathologists often approach the diagnosis of slides in a holistic manner, examining them at various magnifications, panning and zooming in a seemingly haphazard way that they often have a hard time to precisely describe. Hence there has been no clear recipe emerging from numerous interviews with pathologists on how to exactly computer code a diagnosis expert system. Instead, we focused on extracting a small set of histopathological features that were consistently indicated as important by the pathologists and then let the computer figure out how to interpret in a quantitative way the presence or absence of these features over the entire slide. Using the overall pathologists diagnosis (into a class of disease), we train the computer system using advanced machine learning techniques to predict the disease based on the extracted features. By considering the diagnosis of several expert pathologists during the training phase, we insure that the machine is learning a gold standard that will be applied consistently and objectively for all subsequent diagnosis, making them more predictable and reliable. Considering the future of digital pathology, it is essential for a CAD system to produce effective and accurate clinical data. To this effect, there remain many hurdles, including standardization as well as more research into seeking clinical evidences from computer-friendly objective measurements of histological images. Currently the most commonly used staining method is H&E (Hematoxylin and Eosin), but it is extremely difficult to standardize the H&E staining process. Current pathology criteria, category, definitions, and thresholds are all on based pathologists subjective observations. Digital pathology is an emerging field and researchers should bear responsibility not only for developing new algorithms, but also for understanding the meaning of measured quantitative data.

Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features

Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis.

Changes in chromatin structure during processing of wax-embedded tissue sections

The use of immunofluorescence (IF) and fluorescence in situ hybridisation (FISH) underpins much of our understanding of how chromatin is organised in the nucleus. However, there has only recently been an appreciation that these types of study need to move away from cells grown in culture and towards an investigation of nuclear organisation in cells in situ in their normal tissue architecture. Such analyses, however, especially of archival clinical samples, often requires use of formalin-fixed paraffin wax-embedded tissue sections which need addition steps of processing prior to IF or FISH. Here we quantify the changes in nuclear and chromatin structure that may be caused by these additional processing steps. Treatments, especially the microwaving to reverse fixation, do significantly alter nuclear architecture and chromatin texture, and these must be considered when inferring the original organisation of the nucleus from data collected from wax-embedded tissue sections.

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.

Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

Abstract. This paper proposes a digital image analysis method to support quantitative pathology by automatically segmenting the hepatocyte structure and quantifying its morphological features. To structurally analyze histopathological hepatic images, we isolate the trabeculae by extracting the sinusoids, fat droplets, and stromata. We then measure the morphological features of the extracted trabeculae, divide the image into cords, and calculate the feature values of the local cords. We propose a method of calculating the nuclear–cytoplasmic ratio, nuclear density, and number of layers using the local cords. Furthermore, we evaluate the effectiveness of the proposed method using surgical specimens. The proposed method was found to be an effective method for the quantification of the Edmondson grade.