石川　雅浩 （イシカワ　マサヒロ）

コミュニケーション情報 byコメンテータガイド

• コメント

  コンピュータ診断支援は、深層学習の発展に伴って臨床応用への進展が期待される分野です。病理画像、放射線画像、遠隔診断などを対象に臨床で役立つシステムの開発をめざして研究しています。

研究者情報

学位

• 博士(工学)（新潟大学大学院）

ホームページURL

https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201301061080418949

科研費研究者番号

• 70540417

J-Global ID

• 201301061080418949

研究キーワード

• パタン認識   画像処理   分光画像   

現在の研究分野（キーワード）

コンピュータ診断支援は、深層学習の発展に伴って臨床応用への進展が期待される分野です。病理画像、放射線画像、遠隔診断などを対象に臨床で役立つシステムの開発をめざして研究しています。

研究分野

• 情報通信 / 知覚情報処理

研究活動情報

論文

• Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image

  Tanwi Biswas; Hiroyuki Suzuki; Masahiro Ishikawa; Naoki Kobayashi; Takashi Obi

  Journal of Biomedical Optics 28 05 2023年05月 [査読有り]
   

  Significance: Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff's van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming. Aim: The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Approach: H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one. Results: The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber. Conclusions: RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one. Keywords: H&E stained image; Verhoeff’s van Gieson stained image; digital stain conversion; generative adversarial network; hyperspectral imaging; image-to-image translation.
• Effects of dimension reduction of hyperspectral images in skin gross pathology

  Eleni Aloupogianni; Masahiro Ishikawa; Takaya Ichimura; Mei Hamada; Takuo Murakami; Atsushi Sasaki; Koichiro Nakamura; Naoki Kobayashi; Takashi Obi

  Skin Research and Technology 29 2 2023年02月 [査読有り]
   

  Abstract Background Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep‐learning models, dimension reduction is a common preprocessing step. The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. Materials and methods An in‐house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster‐wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. Results The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. Conclusion Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI‐based systems for cancer margin detection at gross level.
• Hyperspectral imaging for tumor segmentation on pigmented skin lesions

  Eleni Aloupogianni; Takaya Ichimura; Mei Hamada; Masahiro Ishikawa; Takuo Murakami; Atsushi Sasaki; Koichiro Nakamura; Naoki Kobayashi; Takashi Obi

  Journal of Biomedical Optics 27 10 2022年10月 [査読有り]
   

  Abstract Significance: Malignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures. Aim: Tumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL. Approach: An HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework. Results: Cross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border. Conclusions: Good performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.
• The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model

  Yuki Hara; Keita Nagawa; Yuya Yamamoto; Kaiji Inoue; Kazuto Funakoshi; Tsutomu Inoue; Hirokazu Okada; Masahiro Ishikawa; Naoki Kobayashi; Eito Kozawa

  Scientific Reports 12 1 2022年08月 

  Abstract We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m². After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.
• Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

  Eleni Aloupogianni; Masahiro Ishikawa; Naoki Kobayashi; Takashi Obi

  Journal of Biomedical Optics 27 6 1 - 28 2022年06月 [査読有り]
   

  Abstract Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue. Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.
• Registration of Histopathological Heterogeneous Stained Images Utilizing GAN Based Domain Adaptation Technique

  Tanwi BISWAS; Hiroyuki SUZUKI; Masahiro ISHIKAWA; Naoki KOBAYASHI; Takashi OBI

  IIEEJ Transactions on Image Electronics and Visual Computing 10 1 19 - 27 2022年06月 [査読有り]
  
• The relationship between imaging features of diffusion-weighted imaging and prognosis of chronic kidney disease

  Tsutomu Inoue; Eito Kozawa; Masahiro Ishikawa; Naoki Kobayashi; Hirokazu Okada

  Kidney International 101 5 1083 - 1083 2022年05月 

  Berchtold et al. reported that corticomedullary differentiation (CMD) abnormality in the apparent diffusion coefficient (ADC) for diffusion-weighted magnetic resonance (MR) imaging affords an index that is significantly correlated with prognosis in chronic kidney disease. 1 We express our gratitude for their kind attention to our previous studies. 2 ,3 The major difference between our study and that of the Berchtold’s group is the setting of the objective variable: we performed multiple regression analysis using the continuous variable, the annual rate of change in estimated glomerular filtration rate (estimated glomerular filtration rate [eGFR] slope; in ml/min per 1.73 m2 per year), as the quantitative objective variable, whereas Berchtold et al. performed Cox proportional analysis using the composite end point of a >30% decline in eGFR or initiation of renal replacement therapy as the qualitative objective variable. The loss of CMD is a feature of images, not only diffusion-weighted images, demonstrating decreased eGFR and correlates with eGFR. 4 Also, from conventional renal MR images, such as T1-weighted images, it is well known that the corticomedullary border is obscured in kidneys with reduced function. The Berchtold’s group reported that CMD abnormality in the ADC was a significant explanatory variable for the end point even after adjustment for eGFR; however, we think it is essential to compare it with other MR images to prove its relation to kidney fibrosis. Whether the significant association between the CMD and the end point set in their article is limited to diffusion-weighted imaging is an important issue to be examined in the future.
• Framework for estimating renal function using magnetic resonance imaging

  Masahiro Ishikawa; Tsutomu Inoue; Eito Kozawa; Hirokazu Okada; Naoki Kobayashi

  Journal of Medical Imaging 9 02 2022年03月 

  Abstract Purpose: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. Approach: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. Results: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. Conclusions: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method.
• Comparison of multiparametric magnetic resonance imaging sequences with laboratory parameters for prognosticating renal function in chronic kidney disease

  Tsutomu Inoue; Eito Kozawa; Masahiro Ishikawa; Daichi Fukaya; Hiroaki Amano; Yusuke Watanabe; Koji Tomori; Naoki Kobayashi; Mamoru Niitsu; Hirokazu Okada

  Scientific Reports 11 1 2021年11月 

  Abstract Magnetic resonance imaging (MRI) is playing an increasingly important role in evaluating chronic kidney disease (CKD). It has the potential to be used not only for evaluation of physiological and pathological states, but also for prediction of disease course. Although different MRI sequences have been employed in renal disease, there are few studies that have compared the different sequences. We compared several multiparametric MRI sequences, and compared their results with the estimated glomerular filtration rate. Principal component analysis showed a similarity between T1 values and tissue perfusion (arterial spin labelling), and between fractional anisotropy (diffusion tensor imaging) and apparent diffusion coefficient values (diffusion-weighted imaging). In multiple regression analysis, only T2* values, derived from the blood oxygenation level-dependent (BOLD) MRI sequence, were associated with estimated glomerular filtration rate slope after adjusting for degree of proteinuria, a classic prognostic factor for CKD. In receiver operating characteristic curve analysis, T2* values were a good predictor of rapid deterioration, regardless of the degree of proteinuria. This suggests further study of the use of BOLD-derived T2* values in the workup of CKD, especially to predict the disease course.
• Application of Magnetic Resonance Imaging in the Evaluation of Nutritional Status: A Literature Review with Focus on Dialysis Patients

  Tsutomu Inoue; Eito Kozawa; Masahiro Ishikawa; Hirokazu Okada

  Nutrients 13 6 2037 - 2037 2021年06月 

  Magnetic resonance imaging (MRI) is indispensable in clinical medicine for the morphological and tomographic evaluation of many parenchymal organs. With varied imaging methods, diverse biological information, such as the perfusion volume and measurements of metabolic products, can be obtained. In addition to conventional MRI for morphological assessment, diffusion-weighted MRI/diffusion tensor imaging is used to evaluate white matter structures in the brain; arterial spin labeling is used for cerebral blood flow evaluation; magnetic resonance elastography for fatty liver and cirrhosis evaluation; magnetic resonance spectroscopy for evaluation of metabolites in specific regions of the brain; and blood oxygenation level-dependent imaging for neurological exploration of eating behavior, obesity, and food perception. This range of applications will continue to expand in the future. Nutritional science is a multidisciplinary and all-inclusive field of research; therefore, there are many different applications of MRI. We present a literature review of MRI techniques that can be used to evaluate the nutritional status, particularly in patients on dialysis. We used MEDLINE as the information source, conducted a keyword search in PubMed, and found that, as a nutritional evaluation method, MRI has been used frequently to comprehensively and quantitatively evaluate muscle mass for the determination of body composition.
• 異なる周辺照度下における平面ディスプレイ視聴時の映像酔い

  ◎山下 大岳; 松浦 歩; 石川 雅浩; 小林 直樹

  生体医工学 59 1 24 - 30 2021年04月 [査読有り]
  
• デジタル画像解析を用いたIgA腎症腎生検糸球体病変定量化

  橋口 明典; 石川 雅浩; 城 謙輔; 坂本 直樹; 山内 暁; 大塚 武; 福西 宗憲; 清水 章; 久野 敏; 片渕 律子; 川村 哲也

  日本腎臓学会誌 62 4 317 - 317 (一社)日本腎臓学会 2020年07月
• Binary Malignancy Classification of Skin Tissue Using Reflectance and Texture Features from Macropathology Multi-Spectral Images

  Eleni Aloupogianni; Hiroyuki SUZUKI; Takaya ICHIMURA; Atsushi SASAKI; Hiroto YANAGISAWA; Tetsuya TSUCHIDA; Masahiro ISHIKAWA; Naoki KOBAYASHI; Takashi OBI

  IIEEJ Transactions on Image Electronics and Visual Computing Vol. 7 No. 2 2019年12月 [査読有り]
  
• Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors

  Hideki Komagata; Erika Kakinuma; Masahiro Ishikawa; Kazuma Shinoda; Naoki Kobayashi

  Sensors 19 20 4581-1 - 4581-20 MDPI 2019年10月 [査読有り]
  
• Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra

  Masahiro Ishikawa; Chisato Okamoto; Kazuma Shinoda; Hideki Komagata; Chika Iwamoto; Kenoki Ohuchida; Makoto Hashizume; Akinobu Shimizu; and Naoki Kobayashi

  Biomedical Optics Express 10 9 4568 - 4588 The Optical Society 2019年09月 [査読有り]
   

  Abstract Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
• Elastic and collagen fibers discriminant analysis using H&E stained hyperspectral images

  Lina Septiana; Hiroyuki Suzuki; Masahiro Ishikawa; Takashi Obi; Naoki Kobayashi; Nagaaki Ohyama; Takaya Ichimura; Atsushi Sasaki; Erning Wihardjo; Dini Andiani

  Optical Review 26 4 369 - 379 The Optical Society of Japan 2019年05月 [査読有り]
  
• Reduced oxygenation but not fibrosis defined by functional magnetic resonance imaging predicts the long-term progression of chronic kidney disease

  Kei Sugiyama; Tsutomu Inoue; Eito Kozawa; Masahiro Ishikawa; Akira Shimada; Naoki Kobayashi; Junji Tanaka; Hirokazu Okada

  Nephrology Dialysis Transplantation 35 6 964 - 970 2018年11月 

  Abstract Background Although chronic hypoxia and fibrosis may be a key to the progression of chronic kidney disease (CKD), a noninvasive means of measuring these variables is not yet available. Here, using blood oxygen level–dependent (BOLD) and diffusion-weighted (DW) magnetic resonance imaging (MRI), we assessed changes in renal tissue oxygenation and fibrosis, respectively, and evaluated their correlation with prognosis for renal function. Methods The study was conducted under a single-center, longitudinal, retrospective observational design. We examined the prognostic significance of T2* values of BOLD-MRI and apparent diffusion coefficient (ADC) values on DW-MRI and other clinical parameters. The rate of decline in estimated glomerular filtration rate (eGFR) was calculated by linear regression analysis using changes in eGFR during the observation period. Results A total of 91 patients were enrolled, with a mean age of 55.8 ± 15.6 years. Among patients, 51 (56.0%) were males and 38 (41.8%) had diabetes mellitus. The mean eGFR was 49.2 ± 28.9 mL/min/1.73 m2 and the mean observation period was 5.13 years. ADC values of DW-MRI but not T2* values of BOLD-MRI were well correlated with eGFR at the initial time point. The mean annual rate of decline in eGFR during the 5-year observation period was −1.92 ± 3.00 mL/min/1.73 m2. On multiple linear regression analysis, the rate of decline in eGFR was significantly correlated with eGFR at the start point, period average amount of proteinuria and T2* values, but not with ADC values (t = 2.980, P = 0.004). Conclusions Reduced oxygenation as determined by low T2* values on BOLD-MRI is a clinically useful marker of CKD progression.
• Joint optimization of multispectral filter arrays and demosaicking for pathological images

  Kazuma SHINODA; Maru KAWASE; Madoka HASEGAWA; Masahiro ISHIKAWA; Hideki KOMAGATA; Naoki KOBAYASHI

  IIEEJ Transactions on Image Electronics and Visual Computing 6 1 13 - 21 The Institute of Image Electronics Engineers of Japan (IIEEJ) 2018年06月 [査読有り]
  
• Feature analysis of cell nuclear chromatin distribution in support of cervical cytology

  Hideki Komagata; Takaya Ichimura; Yasuka Matsuta; Masahiro Ishikawa; Kazuma Shinoda; Naoki Kobayashi; Atsushi Sasaki

  Journal of Medical Imaging 4 4 2017年10月 [査読有り]
   

  Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist's experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.
• Tissue classification of liver pathological tissue specimens image using spectral features

  Emi Hashimoto; Masahiro Ishikawa; Kazuma Shinoda; Madoka Hasegawa; Hideki Komagata; Naoki Kobayashi; Naoki Mochidome; Yoshinao Oda; Chika Iwamoto; Kenoki Ohuchida; Makoto Hashizume

  MEDICAL IMAGING 2017: DIGITAL PATHOLOGY 10140 2017年 [査読有り]
   

  In digital pathology diagnosis, accurate recognition and quantification of the tissue structure is an important factor for computer-aided diagnosis. However, the classification accuracy of cytoplasm is low in Hematoxylin and eosin ( HE) stained liver pathology specimens because the RGB color values of cytoplasm are almost similar to that of fibers. In this paper, we propose a new tissue classification method for HE stained liver pathology specimens by using hyperspectral image. At first we select valid spectra from the image to make a clear distinction between fibers and cytoplasm, and then classify five types of tissue based on the bag of features (BoF). The average classification accuracy for all tissues was improved by 11% in the case of using BoF of RGB and selected spectra bands in comparison with using only RGB. In particular, the improvement reached to 24% for fibers and 5% for cytoplasm.
• 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

  SPIE journal of Medical Imaging 3 2 027502 - 027502 SPIE 2016年06月 [査読有り]
   

  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.
• 観測波長とフィルタ配置を考慮したマルチスペクトルフィルタアレイの最適化手法

  柳悠大; 篠田一馬; 長谷川まどか; 加藤茂夫; 石川雅浩; 駒形英樹; 小林直樹

  電子情報通信学会論文誌D 電子情報通信学会 2016年04月 [査読有り][招待有り]
  
• Optimal Transparent Wavelength and Arrangement for Multispectral Filter Array.

  Yudai Yanagi; Kazuma Shinoda; Madoka Hasegawa; Shigeo Kato; Masahiro Ishikawa; Hideki Komagata; Naoki Kobayashi 0009

  Image Processing: Algorithms and Systems XIV 1 - 5 2016年
• Demosaicking Method for Multispectral Images Based on Spatial Gradient and Inter-channel Correlation

  Shu Ogawa; Kazuma Shinoda; Madoka Hasegawa; Shigeo Kato; Masahiro Ishikawa; Hideki Komagata; Naoki Kobayashi

  IMAGE AND SIGNAL PROCESSING (ICISP 2016) 9680 157 - 166 2016年 [査読有り]
   

  Multispectral images have been studied in various fields such as remote sensing and sugar content prediction in fruits. One of the systems that captures multispectral images uses a multispectral filter array based on a color filter array. In this system, demosaicking processing is required because the captured multispectral images are mosaicked. However, demosaicking is more difficult for multispectral images than for RGB images owing to the low density between the observed pixels in multispectral images. Therefore, we propose a demosaicking method for multispectral images based on spatial gradient and inter-channel correlation. Experimental results demonstrate that our proposed method outperforms the existing methods and is effective.
• Feature Analyses of Trabecular Pattern of Hepatocellular Carcinoma Using Graph Structure

  Hideki Komagata; Masahiro Ishikawa; Naoki Kobayashi; Kazuma Shinoda; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  IIEEJ(The Institute of Image Electronics Engineers of Japan) 2015年12月 [査読有り]
  
• Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes and Trabecular Features

  M. A. Aziz; H. Kanazawa; Y. Murakami; F. Kimura; M. Yamaguchi; T. Kiyuna; Y. Yamashita; A. Saito; M. Ishikawa; N. Kobayashi; T. Abe; A. Hashiguchi

  Analytical Cellular Pathology 2014 1 - 2 2015年06月 [査読有り]
  
• An Efficient Wavelet-Based ROI Coding for Multiple Regions

  Kazuma Shinoda; Naoki Kobayashi; Ayako Katoh; Hideki Komagata; Masahiro Ishikawa; Yuri Murakami; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E98A 4 1006 - 1020 2015年04月 [査読有り]
   

  Region of interest (ROI) coding is a useful function for many applications. JPEG2000 supports ROI coding and can decode ROIs preferentially regardless of the shape and number of the regions. However, if the number of regions is quite large, the ROI coding performance of JPEG2000 declines because the code-stream includes many useless non-ROI codes. This paper proposes a wavelet-based ROI coding method suited for multiple ROIs. The proposed wavelet transform does not access any non-ROIs when transforming the ROIs. Additionally, the proposed method eliminates the need for unnecessary coding of the bits in the higher bit planes of non-ROI regions by adding an ROI map to the code-stream. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio than the ROI coding of JPEG2000. The proposed method can be applied to both max-shift and scaling-based ROI coding.
• An accurate method of extracting fat droplets in liver images for quantitative evaluation

  Masahiro Ishikawa; Naoki Kobayashi; Hideki Komagata; Kazuma Shinoda; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9420 2015年 [査読有り]
   

  The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.
• An accurate method of extracting fat droplets in liver images for quantitative evaluation

  Masahiro Ishikawa; Naoki Kobayashi; Hideki Komagata; Kazuma Shinoda; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  MEDICAL IMAGING 2015: DIGITAL PATHOLOGY 9420 2015年 [査読有り]
   

  The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.
• Multispectral filter array and demosaicking for pathological images

  Kazuma Shinoda; Shu Ogawa; Yudai Yanagi; Madoka Hasegawa; Shigeo Kato; Masahiro Ishikawa; Hideki Komagata; Naoki Kobayashi

  2015 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA) 697 - 703 2015年 [査読有り]
   

  A single-shot multispectral camera equipped with a filter array has the potential to promote a fast and low-cost multispectral imaging system. We focus on the design of a multispectral filter array and demosaicking in this paper and propose a pathology-specific multispectral imaging system. The spectral sensitivities and patterns of the filter array are optimized by using training data of real pathological tissues. The mosaicked image is demosaicked by considering the designed filter array. We show the effectiveness of the proposed imaging system by comparing the recovered spectrum and RGB image with conventional methods.
• Development of a Prototype for Hepatocellular Carcinoma Classification Based on Morphological Features Automatically Measured in Whole Slide Images

  橋口 明典

  ANALYTICAL CELLULAR PATHOLOGY 774 - 779 2014年
• Segmentation of Sinusoids in Hematoxylin and Eosin Stained Liver Specimens Using an Orientation-Selective Filter

  Masahiro Ishikawa; Sercan Taha Ahi; Fumikazu Kimura; Masahiro Yamaguchi; Hiroshi Nagahashi; Akinori Hashiguchi; Michiie Sakamoto

  Open journal of medical imaging vol.3 No.4 pp.144-155  2013年12月 [査読有り]
  
• Automatic segmentation of hepatocellular structure from HE-stained liver tissue

  Masahiro Ishikawa; Sercan Taha Ahi; Yuri Murakami; Fumikazu Kimura; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  Progress in Biomedical Optics and Imaging - Proceedings of SPIE 8676 2013年 [査読有り]
   

  The analysis of hepatic tissue structure is required for quantitative assessment of liver histology. Especially, a cord-like structure of liver cells, called trabecura, has important information in the diagnosis of hepatocellular carcinoma (HCC). However, the extraction of trabeculae is thought to be difficult because liver cells take on various colors and appearances depending on tissue conditions. In this paper, we propose an approach to extract trabeculae from images of hematoxyline and eosin stained liver tissue slide by extracting the rest of trabeculae: sinusoids and stromal area. The sinusoids are simply extracted based on the color information, where the image is corrected by an orientation selective filtering before segmentaion. The stromal area mainly consists of fiber, and often includes lymphocytes densely. Therefore, in the proposed method, fiber region and lymphocytes are extracted separately, then, stromal region is determined based on the extracted results. The determination of stroma is performed based on superpixels, to obtain precise boundaries. Once the regions of sinusoids and stroma are obtained, trabeculae can be segmented as the remaining region. The proposed method was applied to 10 test images of normal and HCC liver tissues, and the results were evaluated based on the manual segmentation. As a result, we confirmed that both sensitivity and specificity of the extraction of trabeculae reach around 90%. © 2013 SPIE.
• An algorithm to evaluate the number of trabecular cell layers using nucleus arrangement applied to hepatocellular carcinoma

  Hideki Komagata; Naoki Kobayashi; Ayako Katoh; Yasuka Ohnuki; Masahiro Ishikawa; Kazuma Shinoda; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  Progress in Biomedical Optics and Imaging - Proceedings of SPIE 8676 2013年 [査読有り]
   

  Recent advances in information technology have improved pathological virtual-slide technology and diagnostic support system studies of pathological images. Diagnostic support systems utilize quantitative indices determined by image processing. In previous studies on diagnostic support systems, carcinomatous areas of breast or lung have been recognized by the feature quantities of nuclear sizes, complexities, and internuclear distances based on graph theory, among other features. Improving recognition accuracy is important for the addition of new feature quantities. We focused on hepatocellular carcinoma (HCC) and investigated new feature quantities of histological images of HCC. One of the most important histological features of HCC is the trabecular pattern. For diagnosing cancer, it is important to recognize the tumor cell trabeculae. We propose a new algorithm for calculating the number of cell layers in histological images of HCC in tissue sections stained by hematoxylin and eosin. For the calculation, we used a Delaunay diagram that was based on the median points of nuclei, deleted the sinusoid and fat droplet regions from the Delaunay diagram, and counted the Delaunay lines while applying a thinning algorithm. Moreover, we experimented with the calculation of the number of cell layers with our method for different histological grades of HCC. The number of cell layers discriminated tumor differentiations and Edmondson grades therefore, our algorithm may serve as an index of HCC for diagnostic support systems. © 2013 SPIE.
• Automatic segmentation of hepatocellular structure from HE-stained liver tissue

  Masahiro Ishikawa; Sercan Taha Ahi; Yuri Murakami; Fumikazu Kimura; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  MEDICAL IMAGING 2013: DIGITAL PATHOLOGY 8676 2013年 [査読有り]
   

  The analysis of hepatic tissue structure is required for quantitative assessment of liver histology. Especially, a cord-like structure of liver cells, called trabecura, has important information in the diagnosis of hepatocellular carcinoma (HCC). However, the extraction of trabeculae is thought to be difficult because liver cells take on various colors and appearances depending on tissue conditions. In this paper, we propose an approach to extract trabeculae from images of hematoxyline and eosin stained liver tissue slide by extracting the rest of trabeculae: sinusoids and stromal area. The sinusoids are simply extracted based on the color information, where the image is corrected by an orientation selective filtering before segmentaion. The stromal area mainly consists of fiber, and often includes lymphocytes densely. Therefore, in the proposed method, fiber region and lymphocytes are extracted separately, then, stromal region is determined based on the extracted results. The determination of stroma is performed based on superpixels, to obtain precise boundaries. Once the regions of sinusoids and stroma are obtained, trabeculae can be segmented as the remaining region. The proposed method was applied to 10 test images of normal and HCC liver tissues, and the results were evaluated based on the manual segmentation. As a result, we confirmed that both sensitivity and specificity of the extraction of trabeculae reach around 90%.
• An algorithm to evaluate the number of trabecular cell layers using nucleus arrangement applied to hepatocellular carcinoma

  Hideki Komagata; Naoki Kobayashi; Ayako Katoh; Yasuka Ohnuki; Masahiro Ishikawa; Kazuma Shinoda; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  MEDICAL IMAGING 2013: DIGITAL PATHOLOGY 8676 2013年 [査読有り]
   

  Recent advances in information technology have improved pathological virtual-slide technology and diagnostic support system studies of pathological images. Diagnostic support systems utilize quantitative indices determined by image processing. In previous studies on diagnostic support systems, carcinomatous areas of breast or lung have been recognized by the feature quantities of nuclear sizes, complexities, and internuclear distances based on graph theory, among other features. Improving recognition accuracy is important for the addition of new feature quantities. We focused on hepatocellular carcinoma (HCC) and investigated new feature quantities of histological images of HCC. One of the most important histological features of HCC is the trabecular pattern. For diagnosing cancer, it is important to recognize the tumor cell trabeculae. We propose a new algorithm for calculating the number of cell layers in histological images of HCC in tissue sections stained by hematoxylin and eosin. For the calculation, we used a Delaunay diagram that was based on the median points of nuclei, deleted the sinusoid and fat droplet regions from the Delaunay diagram, and counted the Delaunay lines while applying a thinning algorithm. Moreover, we experimented with the calculation of the number of cell layers with our method for different histological grades of HCC. The number of cell layers discriminated tumor differentiations and Edmondson grades; therefore, our algorithm may serve as an index of HCC for diagnostic support systems.
• White matter alteration in idiopathic normal pressure hydrocephalus:tract-based spatial statistics study

  T. Hattori; K. Ito; S. Aoki; T. Yuasa; R. Sato; M. Ishikawa; H. Sawaura; M. Hori; H. Mizusawa

  2012年10月 [査読有り]
  
• CTAP 画像中の門脈構造解析に基づいた肝線維化推定法

  石川 雅浩; 内田 日高; 前田 義信; 山本 正信; 五十嵐 正人; 須田 剛士; 野本 実; 青柳 豊

  医用画像情報学会雑誌 23 5 130 - 135 医用画像情報学会 2006年06月 [査読有り]
   

  Chronic liver disease causes health problem such as hepatic failure or hepatocellular carcinoma. As the disease progresses, some fibroses become apparent, and, as such, influence a vascular structure. We have proposed, previously, a method to extract various vascular features from CTAP images. In this paper we suggested that the method improved in this paper should be applicable to CTAP images of 9 patients. It reveals that the vascular-structure analysis can be useful for estimating liver fibrosis level.
• CTAPからの様々な血管特徴抽出のための一手法

  石川 雅浩; 内田 日高; 玉木 徹; 山本 正信; 五十嵐 正人; 須田 剛士; 青柳 豊

  医用画像情報学会雑誌 22 3 210 - 219 医用画像情報学会 2005年 [査読有り]
   

  Liver cirrhosis is usually diagnosed with histological findings of liver biopsy sample. However, liver biopsy, sampling liver tissues from a living donor, is an invasive technique, in which a direct puncture of the liver is needed. On the other hand, higher resolution of recent computed tomography(CT) has a potential to replace histology with objective and digital images, which are available without any invasive techniques. Here we present a system extracting various features of three-dimensional vasculature in the liver from images of CT aiming an objective and noninvasive diagnosis of liver cirrhosis.

書籍

• 「Annual Review 2016」

  井上勉; 小澤栄人; 石川雅浩; 岡田浩一 中外医学社 2016年01月
• Annual Review 2016

  井上勉; 小澤栄人; 石川雅浩; 岡田浩一 中外医学社 2016年01月
• 「情報機器操作入門－らくらくパソコン活用術－」

  山本 正信,三河 賢治,山本 瑞恵,石川 雅浩6番目 学術図書出版社 2007年04月 

  大学生活に必要なコンピュータの基礎知識に関する書籍。その中で、メールの受信や送信についての章を記載。
• 情報機器操作入門－らくらくパソコン活用術－

  山本 正信; 三河 賢治; 山本 瑞恵; 石川 雅浩; 目 学術図書出版社 2007年04月 

  大学生活に必要なコンピュータの基礎知識に関する書籍。その中で、メールの受信や送信についての章を記載。

講演・口頭発表等

• ハイパースペクトル画像を用いたki-67陽性核推定法の開発

  石川 雅浩; 吉田 結真; 中野 夏澄; 黒田 真代; 茅野 秀一; 小林 直樹

  第50回画像電子学会年次大会 2022年08月 口頭発表（一般）
• 病理標本解析のための可視・近赤外ハイパースペクトル画像統合システムの開発

  中村 駿; 石川 雅浩; 小林 直樹

  第50回画像電子学会年次大会 2022年08月 口頭発表（一般）
• マルチスペクトル画像を用いたマクロ病理解析法の一手法  [通常講演]

  ◎山崎 滉仁; 石川 雅浩; 市村 隆也; 浜田 芽衣; 佐々木 惇; 村上 拓生; 竹治 真明; 常深 祐一郎; 中村 晃一郎; 小尾 高史; エレニ アロポジアンニ; 小林 直樹

  第50回画像電子学会年次大会 2022年08月 口頭発表（一般）
• 色素性皮膚病変のハイパースペクトル画像で探索的データ分析  [通常講演]

  Aloupogianni Eleni; Takaya ICHIMURA; Mei HAMADA; Takuo MURAKAMI; Atsushi SASAKI; Koichiro NAKAMURA; 石川 雅浩; Naoki KOBAYASHI; 小尾 高史

  第41回日本医用画像工学会大会 2022年07月 口頭発表（一般）
• Design of a Hyper-Spectral Imaging System for Gross Pathology of Pigmented Skin Lesions  [通常講演]

  Eleni Aloupogianni; Masahiro Ishikawa; Takaya Ichimura; Atsushi Sasaki; Naoki Kobayashi; Takashi Obi

  43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society 2021年11月 口頭発表（一般） online
• Registration of Histopathological Heterogeneous Stained Images Utilizing Gan Based Domain Adaptation Technique  [通常講演]

  Tanwi Biswas; Hiroyuki Suzuki; Masahiro Ishikawa; Naoki Kobayashi; Takashi Obi

  The 7th IIEEJ International Conference on Image Electronics and Visual Computing 2021年09月 口頭発表（一般） online
• 濃度共起行列を用いた腎機能推定法の検討  [通常講演]

  ◎金子 夏実; 石川 雅浩; 井上 勉; 小澤 栄人; 岡田 浩一; 小林 直樹

  画像電子学会 第295回研究会 2021年02月 口頭発表（一般） オンライン開催
• Effect of formalin fixing on chromophore saliency maps derived from multi-spectral macropathology skin images  [通常講演]

  Eleni Aloupogianni; Hiroyuki Suzuki; Takaya Ichimura; Atsushi Sasaki; Hiroto Yanagisawa; Tetsuya Tsuchida; Masahiro Ishikawa; Naoki Kobayashi; Takashi Obi

  Medical Imaging 2021: Digital Pathology 2021年02月 口頭発表（一般） San Diego, California, United States (online)
• Binary Malignancy Classification of Skin Tissue using Reflectance and Texture Features from Macropathology Multi-Spectral Images  [通常講演]

  ◎Takashi OBI; Eleni ALOUPOGIANNI; Hiroyuki SUZUKI; Takaya ICHIMURA; Atsushi SASAKI; Hiroto YANAGISAWA; Tetsuya TSUCHIDA; Masahiro ISHIKAWA; Naoki KOBAYASHI

  International Joint Symposium 2020 The 15th International Workshop on Biomaterials in Interface Science and The 11th Symposium on Innovative Dental-Engineering Alliance (IDEA) 2020年12月 口頭発表（一般） online
• Conversion of H&E Stained to EVG Stained Histological Images using CycleGAN  [通常講演]

  ◎Tanwi BISWAS; Hiroyuki SUZUKI; Masahiro ISHIKAWA; Naoki KOBAYASHI; Takashi OBI

  第39回日本医用画像工学会大会 2020年09月 口頭発表（一般） オンライン開催
• スペクトル情報と色補正を用いた肝病理標本画像の組織分類  [通常講演]

  橋本 江美; 石川 雅浩; 篠田 一馬; 長谷川 まどか; 駒形 英樹; 小林 直樹; 持留 直樹; 岩本 千佳; 大内田 研宙; 小田 義直; 橋爪 誠

  2017年05月 口頭発表（一般）
• Fundamental Technologies for Integration of Multiscale Spatiotemporal Morphology in Multidisciplinary Computational Anatomy - Progress Overview FY2016 -  [通常講演]

  ◎Akinobu Shimizu; Shigeru Nawano; Iwao Hasegawa; Naoki Kobayashi; Hayaru Shono; Atsushi Saito; Hideki Komagata; (Masahiro Ishikawa); Kazuma Shinoda; Marius Linguraru

  The 3rd International Symposium on Multidisciplinary Computational Anatomy 2017年03月 口頭発表（一般）
• 3D reconstruction of pancreatic ducts and collagen fibers from pathological images of pancreas serial sections  [通常講演]

  ◎Hideki Komagata; (Masahiro Ishikawa); Kazuma Shinoda; Naoki Kobayashi; Chika Iwamoto; Kenoki Ohuchida; and Makoto Hashizume

  Image Electronics and Visual Computing Workshop (IEVC) 2017年03月 口頭発表（一般）
• Extraction of glomerulus in whole slide imaging of kidney biopsy specimens  [通常講演]

  ◎(M.Ishikawa); T.Abe; A.Hashiguchi; M.Sakamoto; N.Kobayashi

  SPIE Medical Imaging 2017 2017年02月 ポスター発表
• Tissue classification of liver pathological tissue specimens image using spectral features  [通常講演]

  ◎E.Hashimoto; (M.Ishikawa); K.Shinoda; M.Hasegawa; H.Komagata; N.Kobayashi; N.Mochidome; Y.Oda; K.Ohuchida; M.Hashizume; C.Iwamoto

  SPIE Medical Imaging 2017年02月 ポスター発表
• T2* values in blood oxygen level-dependent MRI may predict prognosis in chronic kidney disease  [通常講演]

  ◎Kei Sugiyama; Tsutomu Inoue; Eiso Kozawa; (Masahiro Ishikawa); Hiroaki Amano; Takeru Kusano; Naoki Kobayashi; Hirokazu Okada

  American Society of Nephrology 2016年11月 口頭発表（一般）
• Demosaicking Method for Multispectral Images Based on Spatial Gradient and Inter-channel Correlation  [通常講演]

  ◎Shu Ogawa; Kazuma Shinoda; Madoka Hasegawa; Shigeo Kato; (Masahiro Ishikawa); Hideki Komagata; Naoki Kobayashi

  MSC2016 2016年09月 口頭発表（一般） Bueno Aires, Argentina IEEE
  
• 腎functional MRIは慢性腎臓病の腎機能予後を予測する  [通常講演]

  ◎杉山圭; 井上勉; 小澤栄人; (石川雅浩); 田中淳司; 小林直樹; 高根裕史; 友利浩司; 瀬戸建; 小野淳; 天野博明; 伊藤悠人; 岡田浩一

  第59回 日本腎臓学会学術総会 2016年06月 ポスター発表
• マルチスペクトル画像を使用した肝病理組織標本の組織分類の検討  [通常講演]

  ◎橋本江美; (石川雅浩); 篠田一馬; 長谷川まどか; 加藤茂夫; 駒形英樹; 小林直樹

  2016年 電子情報通信学会総合大会 情報・システム講演論文集2 2016年03月 口頭発表（一般） 九州大学 電子情報通信学会
  
• マルチスペクトル画像による色素量補正を用いたHE染色肝病理画像中の構造認識  [通常講演]

  ◎(石川雅浩); 橋本江美; 篠田一馬; 長谷川まどか; 加藤茂夫; 駒形英樹; 小林直樹

  2016年 電子情報通信学会総合大会 情報・システム講演論文集2 2016年03月 口頭発表（一般） 九州大学 電子情報通信学会
  
• Apparent diffusion coefficient values of diffusion-weighted magnetic resonance images could predict the prognosis for chronic kidney disease  [通常講演]

  ◎Kei Sugiyama; Tsutomu Inoue; Eito Kozawa; (Masahiro Ishikawa); Naoki Kobayashi; Junji Tanaka; and Hirokazu Okada

  第5回CKD-Frontier 2016年02月 口頭発表（一般）
• Kidney ADC values of diffusion-weighted MRI predict prognosis of CKD  [通常講演]

  ◎Kei Sugiyama; Tsutomu Inoue; Eito Kozawa; (Masahiro Ishikawa); Naoki Kobayashi; Junji Tnaka; Hirokazu Okada

  2016年02月 口頭発表（一般）
• Fundamental Technologies for Integration of Multiscale Spatiotemporal Morphology in Multidisciplinary Computational Anatomy - Plan of Five Years and Progress Overview FY2015 -  [通常講演]

  ◎Akinobu Shimizu; Iwao Hasegawa; Naoki Kobayashi; Hayaru Shono; Shigeru Nawano; Issei Sato; Hideki Komagata; (Masahiro Ishikawa); and Kazuma Shinoda

  2016年02月 口頭発表（一般）
• Optimal transparent wavelength and arrangement for multispectral filter array  [通常講演]

  ◎Yudai Yanagi; Kazuma Shinoda; Madoka Hasegawa; Shigeo Kato; (Masahiro Ishikawa); Hideki Komagata; and Naoki Kobayashi

  IS&T International Symposium on Electronic Imaging 2016年02月 口頭発表（一般）
• EVG染色病理画像における糸球体構造認識  [通常講演]

  ◎(石川雅浩); 本田夏樹; 阿部時也; 橋口明典; 小林直樹

  第１２回 日本病理学会カンファレンス 2015年07月 ポスター発表
• 自動コロニーカウント機能付き透析液清 浄化支援システムの使用評価  [通常講演]

  ◎知久 大輝; (石川 雅浩); 川邉 学; 小林 直樹; 小林 祐子; 藤江 遼平; 本多 仁; 大濱 和也; 高根 裕史; 岡田 浩一,

  2015年05月 口頭発表（一般）
• 機能的MRI(magnetic resonance imaging)を用いた慢性腎臓病評価法の検討  [通常講演]

  ◎井上 勉; 小澤 栄人; (石川 雅浩); 高根 裕史; 友利 浩司; 小林 直樹; 田中 淳 司; 鈴木 洋通; 岡田 浩一,

  2015年04月 口頭発表（一般）
• 腎生検画像における糸球体構造認識  [通常講演]

  ◎(石川雅浩),渡邊すみれ,阿部時也,橋口明典,小林直樹

  医用画像情報学会 2015年02月 口頭発表（一般）
• 3テスラMRIによるASL(arterial spin labeling)を用いた腎灌流量の評価  [通常講演]

  ◎井上 勉; 小澤 栄人; (石川 雅浩); 菊田 知宏; 渡辺 裕輔; 高根 裕史; 小林 直 樹; 鈴木 洋通; 岡田 浩一,

  2015年02月 口頭発表（一般）
• An accurate extracting method of fat droplets in liver images for quantitative evaluation  [通常講演]

  (M. Ishikawa); N. Kobayashi; H. Komagata; K. Shinoda; M. Yamaguchi; T. Abe; A. Hashiguchi; M. Sakamoto

  SPIE Medical Imaging 2015年02月 ポスター発表
• AUTOMATIC DETECTION OF UNUSUAL ALIGNED NUCLEI ON HCC HISTOPATHOLOGICAL IMAGES  [通常講演]

  H. Komagata; N. Kobayashi; (M. Ishikawa); M. Yamaguchi; T. Kiyuna; A. Saito; T. Abe; A. Hashiguchi; M. Sakamoto

  The 4th IIEEJ International Workshop(IEVC2014) 2014年10月 ポスター発表
• Development of a Prototype for Hepatocellular Carcinoma Classification based on Morphological Features Automatically Measured in Whole Slide Images  [通常講演]

  Y. Yamashita; T. Kiyuna; M. Sakamoto; A. Hashiguchi; (M. Ishikawa); Y. Murakami; M. Yamaguchi

  2nd International Congress of the International Academy of Digital Pathology(IADP) 2014年10月 口頭発表（一般）
• Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features  [通常講演]

  M. A. Aziz; H. Kanazawa; Y. Murakami; F. Kimura; M. Yamaguchi; T. Kiyuna; Y. Yamashita; A. Saito; (M. Ishikawa); N. Kobayashi; T. Abe; A. Hashiguchi

  2nd International Congress of the International Academy of Digital Pathology(IADP) 2014年10月 口頭発表（一般）
• Color Processing in Pathology Image Analysis System for Liver Biopsy  [通常講演]

  Y. Murakami; M. Yamaguchi; T. Abe; A. Hashiguchi; Y. Yamashita; A. Saito; (M. Ishikawa); N. Kobayashi; M. Sakamoto

  22nd Color and Imaging Conference(CIC22) 2014年10月 口頭発表（一般）
• HE染色肝病理標本における組織構造定量化によるコンピュータ診断支援  [招待講演]

  ◎(石川 雅浩),坂本 享宇,橋口 明典,山口 雅浩,小林 直樹,村上 百合,齋藤 彰

  第15回情報フォトニクス研究グループ研究会 2014年09月 口頭発表（招待・特別）
• HE染色肝病理標本における組織構造定量化によるがん検出精度向上の検討  [通常講演]

  ◎(石川 雅浩),藤田 悠介,Maulana Abdul Aziz,村上 百合,山口 雅浩,小林 直樹,喜友名 朝春,齋藤 彰,阿部 時也,橋口 明典,坂元 亨宇

  第33回 日本医用画像工学会大会 2014年07月 口頭発表（一般）
• 細胞診断におけるクロマチン分布の偏在性評価法の検討  [通常講演]

  ◎駒形 英樹; 大貫 泰佳; (石川 雅浩); 小林 直樹; 市村 隆也; 後藤 義也; 清水 道生(埼玉医科大学)

  画像電子学会 2014年06月 口頭発表（一般） 東京都新宿区
• A Study of Appropriate Task Stressors forMental Stress Assessment Using Bio-signals  [通常講演]

  ◎N. Kobayashi; N. Ohta; (M. Ishikawa); T. Furusawa

  日本生医工学会大会 2014年06月 ポスター発表
• 透析液清浄化のための生菌数カウント技術の開発  [通常講演]

  ◎(石川 雅浩); 川邊 学; 駒形 英樹; 加納 隆; 小林 直樹(埼玉医科大学)

  画像電子学会 in 広島 2014年02月 口頭発表（一般） 広島県広島市
• 肝病理組織画像における核のグラフ構造特徴量高速計算アルゴリズム  [通常講演]

  ◎駒形英樹; 小林直樹; (石川雅浩); 篠田一馬; 山口雅浩; 阿部時也; 橋口明典; 坂元亨宇

  JAMIT Frontier 2014 2014年01月 ポスター発表 沖縄県那覇市
• 機械学習を用いた肝生検病理画像からの脂肪滴抽出のための一手法  [通常講演]

  ◎(石川雅浩); 小林直樹; 駒形英樹; 山口雅浩; 阿部時也; 橋口明典; 坂元亨宇

  JAMIT Frontier 2014 2014年01月 ポスター発表 沖縄県那覇市
• HE染色肝病理組織標本における索状構造の配列の乱れ定量化法の提案  [通常講演]

  ◎(石川 雅浩); 福井智也; 藤田悠介; 村上百合; 山口雅浩; 阿部時也; 橋口明典; 坂本亨宇; 齋藤彰

  日本医用画像工学会 大会 2013年08月 口頭発表（一般）
• Automatic segmentation of hepatocellular structure from HE-stained liver tissue  [通常講演]

  ◎(MasahiroIshikawa); Sercan Taha Ahi; Yuri Murakami; Fumikazu Kimura; Masahiro Yamaguchi; Tokiya Abe; Akinori Hashiguchi; Michiie Sakamoto

  SPIE Medical Imaging 2013: Digital Pathology 2013年02月 ポスター発表
• 肝病理組織における索状構造解析のための形態的特徴の自動計測  [通常講演]

  ◎(石川雅浩); 福井智也; 村上百合; 山口雅浩; 阿部時也; 橋口明典; 坂元亨宇

  電子情報通信学会 医用画像研究会 2013年01月 ポスター発表 沖縄

MISC

• デジタル画像解析を用いたIgA腎症糸球体病変定量化

  橋口 明典; 石川 雅浩; 城 謙輔; 坂本 直樹; 山内 暁; 大塚 武; 福西 宗憲; 清水 章; 片渕 律子; 川村 哲也 日本病理学会会誌 110 (1) 327 -327 2021年03月
• デジタル画像解析を用いたIgA腎症腎生検糸球体病変定量化

  橋口 明典; 石川 雅浩; 城 謙輔; 坂本 直樹; 山内 暁; 大塚 武; 福西 宗憲; 清水 章; 久野 敏; 片渕 律子; 川村 哲也 日本腎臓学会誌 62 (4) 317 -317 2020年07月
• 腎生検糸球体病変定量化の試み

  橋口 明典; 石川 雅浩; 城 謙輔; 清水 章; 久野 敏; 片渕 律子; 川村 哲也 日本病理学会会誌 109 (1) 435 -435 2020年03月
• HE染色肝病理組織標本における方位選択性フィルタを用いた類洞抽出法の提案

  石川雅浩 信学技報, 2012 2012年
• CTAPからの形状特徴量抽出のための一手法（受賞論文要約）

  石川 雅浩; 内田 日高; 玉木 徹; 山本 正信; 五十嵐 正人; 須田 剛士; 青柳 豊 新潟大学工学部研究報告 56 (56) 1 -2 2007年03月
• D-16-8 肝臓CTからの肝硬変推定のための一手法(D-16. 医用画像, 情報・システム2)

  石川 雅浩; 内田 日高; 玉木 徹; 山本 正信; 五十嵐 正人; 須田 剛士; 青柳 豊 電子情報通信学会総合大会講演論文集 2005 (2) 2005年03月
• CTAPからの形状特徴量抽出のための一手法

  石川 雅浩; 内田 日高; 玉木 徹; 山本 正信; 五十嵐 正人; 須田 剛士; 青柳 豊; Ishikawa Masahiro; Uchida Hidaka; Tamaki Toru; Yamamoto Masanobu; Igarashi Masato; Suda Takeshi; Aoyagi Yutaka 医用画像情報学会雑誌 22 (3) 210 -219 2005年03月
• 信念伝播アルゴリズムを用いた医用画像処理

  石川 雅浩; 玉木 徹; 山本 正信 映像情報メディア学会大会講演予稿集 2003 (0) 4 -4 2003年

産業財産権

• 特願2017-165001:画像処理装置、画像処理方法、画像処理プログラム  2017年08月30日

  石川雅浩, 小林直樹, 中野和也
• 特開2015-154718(P2015-154718A):自動コロニー数カウント法、自動コロニー数カウントプログラム  2015年08月27日

  石川雅浩, 川邉学, 小林直樹, 加納隆, 駒形英樹  埼玉医科大学

受賞

• 2022年06月 画像電子学会 優秀論文賞
  
• 2017年03月 IEVC IEVC2017 Best Paper Award
   JPN international_society
• 2015年02月 SPIE Medical Imaging(CUMU LAUDE)
   USA international_society
• 2014年09月 IEVC2014 Best Paper Award
   THA international_society
• 2013年12月 日本医用画像工学会 大会奨励賞
   JPN
• 2013年05月 MI研究奨励賞(電子情報通信学会 医用画像研究会)
   JPN
• 2013年02月 Poster Award Honorable mention(SPIE Medical Imaging 2013)
  
• 2005年06月 金森奨励賞(医用画像情報学会)
   JPN

共同研究・競争的資金等の研究課題

• 自家蛍光を用いた未染色病理標本のデジタル染色定量化法の開発

  日本学術振興会：科学研究費 若手研究(B)

  研究期間 : 2017年04月 - 現在 

  代表者 : 石川雅浩

   

  自家蛍光画像を用いた未染色標本のデジタル染色法の開発を目指して研究を行った．デジタル染色に適した自家蛍光画像の決定，自家蛍光画像と染色画像の位置合わせ，深層学習を用いたデジタル染色の実現という成果が得られた．しかし，自家蛍光が確認できない細胞核が存在することが判明し，今後更なる手法の改善が必要なことが明らかとなった．
• IgA腎症予後分類のブラッシュアップのための前向きコホート研究の推進とハイリスク患者の透析移行を阻止する治療法の開発

  AMED：

  研究期間 : 2017年04月 - 現在 

  代表者 : 石川雅浩
• スーパーピクセルを用いた病理画像中の構造認識法の開発

  日本私立学校振興・共済事業団：

  研究期間 : 2015年04月 - 現在 

  代表者 : 石川雅浩
• がん超早期診断・治療機器の総合研究開発

  独立行政法人 新エネルギー・産業技術総合開発機構：

  研究期間 : 2013年04月 - 現在
• 可視・近赤外分光情報に基づく細胞識別法の創出

  日本学術振興会：科学研究費助成事業 基盤研究(B)

  研究期間 : 2023年04月 -2028年03月 

  代表者 : 石川 雅浩; 中野和也; 橋口明典; 山口雅浩
• がんゲノム医療のための革新的核酸抽出技術の開発

  日本学術振興会：挑戦的研究(萌芽)

  研究期間 : 2021年07月 -2024年03月 

  代表者 : 石川 雅浩; 鈴木 賢治; 小林 直樹; 橋口 明典
• 腎functional MRIとAIによる慢性腎臓病の進行リスク評価システムの構築

  AMED：

  研究期間 : 2019年04月 - 現在
• 多元計算解剖学における形態情報統合の基盤技術

  日本学術振興会：科学研究費助成事業 新学術領域研究(研究領域提案型)

  研究期間 : 2014年07月 -2019年03月 

  代表者 : 清水 昭伸; 縄野 繁; 小林 直樹; 庄野 逸; 長谷川 巖; 斉藤 篤; Linguraru George Marius; 篠田 一馬; 駒形 英樹; 石川 雅浩

   

  主な研究成果は以下の通りである．まず，時系列データからの時空間統計モデル構築法を提案した．具体的には，少数サンプルからのパラメータ推定，時間軸に沿った滑らか制約や入れ子と隣接制約を導入したモデル化，トポロジー変化を表現可能なモデル化である．これらは，ヒト胚子や小児の時空間統計モデル構築に用いた．二つ目は，辞書学習や深層学習による超解像であり，CTの超解像化に用いた．三つ目は，機械学習を用いた病理画像からの組織の3次元再構成である．四つ目のハイパースペクトル画像処理では，組織の分類精度のさらなる向上を目指した．五つ目は，セグメンテーションと統計モデルの同時最適化法であり，CTの臓器認識に用いた．
• 移植腎病理診断におけるプレクリニカルな診断支援システムの開発

  日本学術振興会：科学研究費 若手(B)

  研究期間 : 2015年04月 -2018年03月 

  代表者 : 石川雅浩

   

  本研究では，移植腎病理組織画像中の構造認識と機械学習を用いた機能推定法の開発を行っている．本事業では，Whole Slide Imagingを対象に移植腎病理標本中の皮質・髄質領域の判別と糸球体の抽出と周辺線維量の定量化法を開発した．また，スペクトル画像を用いた構造認識法についても検討し，従来のRGB値を用いた手法よりも高精度に組織構造認識が可能なことを示した．最後に，臨床データ等を用いて移植腎機能推定を行い，最大で85%の精度で腎機能推定の可能性を示した．
• 清浄化予測機能付き透析液管理システムの開発

  研究成果最適展開支援プログラム（A-STEP）探索タイプ：

  研究期間 : 2014年12月 -2015年03月 

  代表者 : 石川雅浩

その他のリンク

researchmap

https://researchmap.jp/7000006533