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

Masahiro Ishikawa, Yuri Murakami, Sercan Taha Ahi, Masahiro Yamaguchi, Naoki Kobayashi, Tomoharu Kiyuna, Yoshiko Yamashita, Akira Saito, Tokiya Abe, Akinori Hashiguchi, Michiie Sakamoto
2016 Journal of Medical Imaging  
Ishikawa, a,b, * Yuri Murakami, a Sercan Taha Ahi, a Masahiro Yamaguchi, a Naoki Kobayashi, b Tomoharu Kiyuna, c Yoshiko Yamashita, c Akira Saito, d,e Tokiya Abe, f Akinori Hashiguchi, f and Michiie Sakamoto  ...  Tomoharu Kiyuna is a senior expert at the Medical Solutions Division of NEC Corporation.  ... 
doi:10.1117/1.jmi.3.2.027502 pmid:27335894 pmcid:PMC4891529 fatcat:xdskqpzcnnbgvf5up2lx4mxtza

Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features

Maulana Abdul Aziz, Hiroshi Kanazawa, Yuri Murakami, Fumikazu Kimura, Masahiro Yamaguchi, Tomoharu Kiyuna, Yoshiko Yamashita, Akira Saito, Masahiro Ishikawa, Naoki Kobayashi, Tokiya Abe, Akinori Hashiguchi (+1 others)
2014 Analytical Cellular Pathology  
Hepatocellular carcinoma (HCC) is a malignant tumor with hepatocellular differentiation and one of the most common cancers in the world. This type of cancer is often diagnosed when the survival time is measured in months causing high death rates [1] . For the purpose of supporting histopathology diagnosis of HCC, we have developed an experimental system of "feature measurement software for liver biopsy" [2] . The system provides pathologists with the quantitative measurement of tissue
more » ... using a digital slide of hematoxylin-eosin (HE) stained liver tissue specimen, as well as the HCC detection based on those measurement results. In this study, we are focusing on the classification process of HCC images in the system. Previously, Kiyuna et al. [3] had introduced an automatic classification of HCC images based on 13 types of nuclear and structural features, where each feature consists of 6 statistical distributions. In order to improve the classification performance, we have developed methods to segment the liver tissue and quantify additional tissue features such as trabecular morphology [4]. This paper reports the evaluation results on the impact of the segmentation and the additional features in the HCC detection performance. Method We enhanced the classification process presented in [3] by including 11 features of tissue changes (i.e., features related to fatty change, cytoplasm colors, cell clearness index, and stroma) and 10 features of trabecular (e.g., nuclei-cytoplasmic ratio, irregularity of sinusoid, and trabecular arrangements). Furthermore, we apply a mask obtained by the stroma segmentation before calculating the 13 types of nuclear and structural features such that those features are derived from hepatocytes only, thus generating in total 177 features. The experiments were performed on a collection of region-ofinterest ROI) images extracted from HE stained whole slide images (WSI), consisting of 1054 ROIs of HCC biopsy samples (504 negatives and 550 positives) and 1076 ROIs of HCC surgically resected samples (533 negatives and 543 Specificity Biopsy sample Biopsy sample 78 nuclei (unmasked) 86.36% 88.29% Surgery sample Surgery sample 78 nuclei (unmasked) 88.21% 87.99% Combination of biopsy and surgery sample Biopsy sample 78 nuclei (unmasked) 84.73% 91.87% 78 nuclei (masked) 85.27% 90.67% 72 nuclei (masked) + 21 new features 88.18% 91.87% Surgery sample 78 nuclei (unmasked) 88.95% 87.62% 78 nuclei (masked) 89.50% 87.62% 72 nuclei (masked) + 21 new features 91.34% 89.68% positives). In the process, we made some combinations on the sets of features and sets of training data from both biopsy and surgery samples. As for the classification, we used 5-fold cross validation support vector machine (SVM) with LibSVM as our library. Results The results of classification experiment are summarized in Table 1 . Our experiments show that combinations of the new features with the nuclei and structural features can improve the accuracy for about 1-3% depending on the type of training and test data. For example, in biopsy samples, the sensitivity is improved from 84.7% to 88.2% while the specificity is unchanged (91.9%). Furthermore, in surgery samples, the detection rate for the well-differentiated tumors (Edmondson grade 1) is improved from 65.0% to 77.5% by the addition of new features. Nevertheless, the masking process on the nuclei features brings different effect on biopsy and surgery samples, but it facilitates the reliability of the nuclei features since falsely detected nuclei are removed from the quantification. Conclusion The combination of nuclear, trabecular, and other tissue features enables improved classification rate in HCC detection using SVM. Even though the image characteristics are different in biopsy and surgically resected samples, the same classification system gives good performance in both samples. The HCC classification scheme introduced in this paper is implemented in the prototype "feature measurement software for liver biopsy, " and the probability of HCC is visualized for every ROI in the WSI. It will support pathologists in the HCC diagnosis along with the quantitative measurements of tissue morphology.
doi:10.1155/2014/726782 fatcat:nome6ka55jeuxbbt5srlfgcsye

List of Contents, Author Index, Reviewers and Editorial Board

2017 Tribology Online  
Lubricated Condition 653-660 Takeshi Yamaguchi and Kazuo Hokkirigawa Article Development of a High-Power Two-Roller Traction Tester and Measurement of Traction Curves 661-674 Hirofumi Itagaki, Hiroki Hashiguchi  ...  Clara Morita-Imura and Kaori Niki A Constitutive Friction Law for Sheet-Bulk Metal Forming 614-622 Florian Beyer and Kai Willner Wear Debris Analysis of Seizure Behaviors of PEEK Materials in Oil 623-631 Tomoharu  ... 
doi:10.2474/trol.11.iv fatcat:cdwaijkeqvgh3if6ox3srtfuxa

Author index

2018 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama)   unpublished
., 99 Hashiguchi Hiroyuki, 37, 1154, 2134 Hassan M.  ...  ., 66, 176, 1948, 1963, 1968 Shi Jian-Cheng, 31, 1204 Shi Wei, 1851 Shi Wenzhong, 2196 Shibagaki Nobuhiko, 1693 Shibazaki Toshihiko, 2455 Shields Sidney, 940 Shih Tien-Tsorng, 1184 Shimada Tomoharu, 661  ... 
doi:10.23919/piers.2018.8598111 fatcat:ekwwlc6wwbec7paa5rtjmdgcga

Page 2170 of Mathematical Reviews Vol. , Issue Index [page]

Mathematical Reviews  
(Summary) 2004e:81055 81Q60 Sakamoto, Ryoji (with Ishibuchi, Hisao; Nakashima, Tomoharu) Learning fuzzy rules from iterative execution of games.  ...  (Edith Padron) 2004k:53137 53D17 (58A50, 81S10) Sakakibara, Naoto (with Hashiguchi, Kosaburo; Jimbo, Shuji) Equivalence of regular binoid expressions and regular expressions denoting binoid languages over  ... 

Abstracts of the 1st Congress of the International Academy of Digital Pathology August 3–5, 2011 Quebec city, Canada

Bernard Têtu
2011 Analytical Cellular Pathology  
The e-Pathologist Cancer Diagnosis Assistance System for gastric biopsy tissues Maki Ogura 1,2 , Akira Saito 1 , Hans Peter Gra 3 , Eric Cosatto 3 , Christopher Malon 3 , Atsushi Marugame 1 , Tomoharu  ...  Quantifi cation of liver fi brosis by whole slide image analysis Tokiya Abe 1 , Ken Yamazaki 1 , Akinori Hashiguchi 1 , Hidetsugu Saito 2,3 and Michiie Sakamoto 1 Background: Fibrosis progression and architectural  ... 
doi:10.1155/2011/564210 pmcid:PMC4605615 fatcat:uhk7ljvkdfca7dlnyaukose77u

Integrated Physiology/Obesity

2017 Diabetes  
doi:10.2337/db17-1871-2152 fatcat:j5qxhugk2ja2lci6oli72bhcqa