Tokiya Abe

Quantification of collagen and elastic fibers using whole-slide images of liver biopsy specimens

Histological evaluation of fibrosis after a liver biopsy is crucial for evaluating the pathology of patients with chronic liver disease. Previous studies have reported quantitative analyses of fibrosis using images of collagen‐stained sections. However, analysis of these studies requires manual selection of the region of interest. In addition, the quantification of elastic fibers is not considered. The present study was conducted in order to measure both the collagen and elastic fiber area ratios using Elastica van Gieson‐stained whole‐slide images (WSIs) of liver biopsy specimens. High‐resolution WSIs provide precise color classification, enabling accurate detection of even fine collagen and elastic fibers. To minimize the influence of pre‐existing fibrous tissue, median area ratios of the collagen and elastic fibers were independently calculated from the image tiles of the WSIs. These median area ratios were highly concordant with area ratios after the pre‐existing fibrous tissues were manually trimmed from the WSI. Further, these median area ratios were correlated with liver stiffness as measured by transient elastography (collagen: ru2009=u20090.73 [Pu2009<u20090.01], elastic: ru2009=u20090.53 [Pu2009<u20090.01]). Our approach to quantifying liver fibrosis will serve as an effective tool to evaluate liver diseases in routine practice.

Color correction of pathological images for different staining-condition slides

The color of hematoxylin & eosin (H&E) stained tissue image varies with the staining conditions and with the characteristics of the microscope and imaging devices. This color variation affects the diagnostic examination. This paper proposes a color correction method for images of pathological slides prepared under inappropriate staining-condition. In the proposed method, the spectral transmittance obtained by a multispectral digital camera is utilized to estimate the amount of dye for each pixel. Subsequently the amount of dye is adjusted based on the chemical kinetic equation by digital process, so that the dye amount distribution would be equivalent to that of the reference slide, which is prepared under ideal staining conditions. Through the experiments using the H&E stained slides of liver tissue, the color was almost successfully corrected by the proposed method.

Multispectral image enhancement for H&E stained pathological tissue specimens

The presence of a liver disease such as cirrhosis can be determined by examining the proliferation of collagen fiber from a tissue slide stained with special stain such as the Massons trichrome(MT) stain. Collagen fiber and smooth muscle, which are both stained the same in an H&E stained slide, are stained blue and pink respectively in an MT-stained slide. In this paper we show that with multispectral imaging the difference between collagen fiber and smooth muscle can be visualized even from an H&E stained image. In the method M KL bases are derived using the spectral data of those H&E stained tissue components which can be easily differentiated from each other, i.e. nucleus, cytoplasm, red blood cells, etc. and based on the spectral residual error of fiber weighting factors are determined to enhance spectral features at certain wavelengths. Results of our experiment demonstrate the capability of multispectral imaging and its advantage compared to the conventional RGB imaging systems to delineate tissue structures with subtle colorimetric difference.

Digital staining of pathological tissue specimens using spectral transmittance

Staining of tissue specimens is a classical procedure in pathological diagnosis to enhance the contrast between tissue components such that identification and classification of these components can be easily performed. In this paper, a framework for digital staining of pathological specimens using the information derived from the L-band spectral transmittance of various pathological tissue components is introduced, particularly the transformation of a Hematoxylin and Eosin (HE) stained specimen to its Masson-Trichrome (MT) stained counterpart. The digital staining framework involves the classification of tissue components, which are highlighted when the specimen is actually stained with MT stain, e.g. fibrosis, from the HE-stained image; and the linear mapping between specific sets of HE and MT stained transmittance spectra through pseudo-inverse procedure to produce the LxL transformation matrices that will be used to transform the HE stained transmittance to its equivalent MT stained transmittance configuration. To generate the digitally stained image, the decisions of multiple quadratic classifiers are pooled to form the weighting factors for the transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) for the implementation of digital staining in the pathological context.

Correction: Elastin Fiber Accumulation in Liver Correlates with the Development of Hepatocellular Carcinoma.

[This corrects the article DOI: 10.1371/journal.pone.0154558.].

Whole Slide Image Analysis System for Quantification of Liver Fibrosis

1Department of Pathology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan 2Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan 3Medical Solutions Division, NEC Corporation, 7-1, Shiba 5-chome, Minato-ku, Tokyo 108-001, Japan 4Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, 1-26-1 Kyonan-cho, Musashino, Tokyo 180-8610, Japan

Digital staining of pathological images: dye amount correction for improved classification performance

Physical staining is indispensable in pathology. While physical staining uses chemicals, digital staining exploits the differing spectral characteristics of the different tissue components to simulate the effect of physical staining. Digital staining for pathological images involves two basic processes: classification of tissue components and digital colorization whereby the classified tissue components are impressed with colors associated to their reaction to specific dyes. Spectral features, i.e. spectral transmittance, of the different tissue structures are dependent on the staining condition of the tissue slide. Thus, if the staining condition of the test image is different, classification result is affected, and the resulting digitally-stained image may not reflect the desired result. This paper shows that it is possible to obtain robust classification results by correcting the dye amount of each test-image pixel using Beer Lamberts Law. Also the effectiveness of such technique to be incorporated to the current digital staining scheme is investigated as well.

Quantitative assessment of liver fibrosis reveals a nonlinear association with fibrosis stage in nonalcoholic fatty liver disease

Accurate staging of liver fibrosis is crucial to guide therapeutic decisions for patients with nonalcoholic fatty liver disease (NAFLD). Digital image analysis has emerged as a promising tool for quantitative assessment of fibrosis in chronic liver diseases. We sought to determine the relationship of histologic fibrosis stage with fiber amounts quantified in liver biopsy specimens for the better understanding of NAFLD progression. We measured area ratios of collagen and elastin fibers in Elastica van Gieson‐stained biopsy tissues from 289 patients with NAFLD from four hospitals using an automated computational method and examined their correlations with Brunts fibrosis stage. As a secondary analysis, we performed multivariable logistic regression analysis to assess the associations of the combined area ratios of collagen and elastin with noninvasive fibrosis markers. The combined fiber area ratios correlated strongly with Brunts stage (Spearman correlation coefficient, 0.78; P < 0.0001), but this relationship was nonlinear (P = 0.007) with striking differences between stage 4 (median area ratios, 12.3%) and stages 0‐3 (2.1%, 2.8%, 4.3%, and 4.8%, respectively). Elastin accumulation was common in areas of thick bridging fibrosis and thickened venous walls but not in areas of perisinusoidal fibrosis. The highest tertile of the combined fiber area ratios was associated with the fibrosis‐4 index and serum type IV collagen 7s domain (7s collagen) levels, whereas the upper two tertiles of the fiber amounts significantly associated with body mass index, aspartate aminotransferase, and 7s collagen in the multivariable analysis. Conclusion: Quantitative fibrosis assessment reveals a nonlinear relationship between fibrosis stage and fiber amount, with a marked difference between stage 4 and stage 3 and much smaller differences among stages 0‐3, suggesting a heterogeneity in disease severity within NAFLD‐related cirrhosis. (Hepatology Communications 2018;2:58–68)

MULTIFRACTAL COMPUTATION FOR NUCLEAR CLASSIFICATION AND HEPATOCELLULAR CARCINOMA GRADING

Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper pro- poses a textural feature descriptor and a novel computa- tional method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural fea- ture descriptor observes local and spatial characteristic s of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fi ber region detection, and liver cell nuclei classification. Fou r categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classi - fication method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liv er cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accu- racy.