Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Deep learning system for automatic detection of bladder tumors in cystoscopic images Invited Reviewed

Mutaguchi J., Oda M., Ueda S., Kinoshita F., Naganuma H., Matsumoto T., Lee K., Monji K., Kashiwagi K., Takeuchi A., Shiota M., Inokuchi J., Mori K., Eto M.

EUROPEAN UROLOGY Vol. 79 page： S1022 - S1023 2021.6

Development of Diagnosis Assistant AI for COVID-19 Patients Invited Reviewed Open Access

ODA Masahiro, ZHENG Tong, HAYASHI Yuichiro, MORI Kensaku

Medical Imaging Technology Vol. 39 ( 1 ) page： 13 - 19 2021.1

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japanese Society of Medical Imaging Technology

<p>We introduce AIs for diagnosis assistance of COVID-19 patients from CT volumes that were developed in Nagoya University. Novel coronavirus disease 2019（COVID-19）spreads over the world causing the large number of infected patients and deaths. Diagnosis assistance by AI is effective for diagnosing the large number of patients that are caused by infective diseases. We developed diagnosis assistant AIs for COVID-19 cases that evaluate the typical-ness of COVID-19 case based on image appearances from a CT volume. We developed three essential methods for the AIs including the lung region segmentation method, the clustering method of lung region, and the COVID-19 typical-ness evaluation method. To develop the AIs, we utilized huge number of medical images stored in the cloud platform of medical bigdata. We confirmed our AI has high performance for diagnosis assistance in the evaluations using CT volumes of real COVID-19 patients.</p>

DOI： 10.11409/mit.39.13

Open Access

CiNii Research

Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection from MRI Invited International coauthorship

Sadayuki Ito, Kei Ando, Kazuyoshi Kobayashi, Hiroaki Nakashima Masahiro Oda, Masaaki Machino, Shunsuke Kanbara, Taro Inoue, Hidetoshi Yamaguchi, Hiroyuki Koshimizu, Kensaku Mori, Naoki Ishiguro, Shiro Imagama

Spine Vol. 46 ( 2 ) page： 95 - 100 2021.1

Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation Invited Reviewed Open Access

Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2021

Context encoder guided self-supervised siamese depth estimation based on stereo laparoscopic images Invited Reviewed

Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling Vol. 11598 2021

Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation Invited Reviewed

Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotsugu Takabatake, Masaki Mori, Hirotoshi Honma, Hiroshi Natori, Kensaku Mori

Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling Vol. 11598 page： 115980N-1 - 7 2021

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty Invited Reviewed Open Access

Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

LNCS12969 Vol. 12969 LNCS page： 88 - 97 2021

CURRENT STATUS AND FUTURE PERSPECTIVE ON ARTIFICIAL INTELLIGENCE FOR LOWER ENDOSCOPY Invited Reviewed Open Access

MISAWA Masashi, KUDO Shin-ei, MORI Yuichi, MAEDA Yasuharu, OGAWA Yushi, ICHIMASA Katsuro, KUDO Toyoki, WAKAMURA Kunihiko, HAYASHI Takemasa, MIYACHI Hideyuki, BABA Toshiyuki, ISHIDA Fumio, ITOH Hayato, ODA Masahiro, MORI Kensaku

GASTROENTEROLOGICAL ENDOSCOPY Vol. 63 ( 7 ) page： 1402 - 1416 2021

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japan Gastroenterological Endoscopy Society

<p>The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed） which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.</p>

DOI： 10.11280/gee.63.1402

Scopus

CiNii Research

1. Computer-assisted Diagnosis of COVID-19 CT Images Based on Machine Learning Invited Open Access

Mori Kensaku

The Journal of The Institute of Image Information and Television Engineers Vol. 75 ( 3 ) page： 325 - 329 2021

Clinical and Genetic Characteristics of 18 Patients from 13 Japanese Families with CRX-associated retinal disorder: Identification of Genotype-phenotype Association Invited Reviewed Open Access

Fujinami-Yokokawa Y., Fujinami K., Kuniyoshi K., Hayashi T., Ueno S., Mizota A., Shinoda K., Arno G., Pontikos N., Yang L., Liu X., Sakuramoto H., Katagiri S., Mizobuchi K., Kominami T., Terasaki H., Nakamura N., Kameya S., Yoshitake K., Miyake Y., Kurihara T., Tsubota K., Miyata H., Iwata T., Tsunoda K., Nishimura T., Hayashizaki Y., Kondo M., Shimozawa N., Horiguchi M., Yamamoto S., Kuze M., Naoi N., Machida S., Shimada Y., Nakamura M., Fujikado T., Hotta Y., Takahashi M., Mochizuki K., Murakami A., Kondo H., Ishida S., Nakazawa M., Hatase T., Matsunaga T., Maeda A., Noda K., Tanikawa A., Yamamoto S., Yamamoto H., Araie M., Aihara M., Nakazawa T., Sekiryu T., Kashiwagi K., Kosaki K., Piero C., Fukuchi T., Hayashi A., Hosono K., Mori K., Tanaka K., Furuya K., Suzuki K., Kohata R., Yanagi Y., Minegishi Y., Iejima D., Suga A., Rossmiller B.P., Pan Y., Oshima T., Nakayama M., Teruyama Y., Yamamoto M., Minematsu N., Sanbe H., Mori D., Kijima Y., Mawatari G., Kurata K., Yamada N., Itoh M., Kawaji H., Murakawa Y.

Scientific Reports Vol. 10 ( 1 ) 2020.12

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Scientific Reports

Inherited retinal disorder (IRD) is a leading cause of blindness, and CRX is one of a number of genes reported to harbour autosomal dominant (AD) and recessive (AR) causative variants. Eighteen patients from 13 families with CRX-associated retinal disorder (CRX-RD) were identified from 730 Japanese families with IRD. Ophthalmological examinations and phenotype subgroup classification were performed. The median age of onset/latest examination was 45.0/62.5 years (range, 15–77/25–94). The median visual acuity in the right/left eye was 0.52/0.40 (range, −0.08–2.00/−0.18–1.70) logarithm of the minimum angle of resolution (LogMAR) units. There was one family with macular dystrophy, nine with cone-rod dystrophy (CORD), and three with retinitis pigmentosa. In silico analysis of CRX variants was conducted for genotype subgroup classification based on inheritance and the presence of truncating variants. Eight pathogenic CRX variants were identified, including three novel heterozygous variants (p.R43H, p.P145Lfs*42, and p.P197Afs*22). A trend of a genotype-phenotype association was revealed between the phenotype and genotype subgroups. A considerably high proportion of CRX-RD in ADCORD was determined in the Japanese cohort (39.1%), often showing the mild phenotype (CORD) with late-onset disease (sixth decade). Frequently found heterozygous missense variants located within the homeodomain underlie this mild phenotype. This large cohort study delineates the disease spectrum of CRX-RD in the Japanese population.

DOI： 10.1038/s41598-020-65737-z

Open Access

Scopus

Clinical characteristics in patients with ossification of the posterior longitudinal ligament: A prospective multi-institutional cross-sectional study Invited Reviewed Open Access

Hirai T., Yoshii T., Ushio S., Mori K., Maki S., Katsumi K., Nagoshi N., Takeuchi K., Furuya T., Watanabe K., Nishida N., Watanabe K., Kaito T., Kato S., Nagashima K., Koda M., Ito K., Imagama S., Matsuoka Y., Wada K., Kimura A., Ohba T., Katoh H., Matsuyama Y., Ozawa H., Haro H., Takeshita K., Watanabe M., Matsumoto M., Nakamura M., Yamazaki M., Okawa A., Kawaguchi Y.

Scientific Reports Vol. 10 ( 1 ) 2020.12

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Scientific Reports

Ossification of the posterior longitudinal ligament (OPLL) can occur throughout the entire spine and can sometimes lead to spinal disorder. Although patients with OPLL sometimes develop physical limitations because of pain, the characteristics of pain and effects on activities of daily living (ADL) have not been precisely evaluated in OPLL patients. Therefore, we conducted a multi-center prospective study to assess whether the symptoms of cervical OPLL are different from those of cervical spondylosis (CS). A total of 263 patients with a diagnosis of cervical OPLL and 50 patients with a diagnosis of CS were enrolled and provided self-reported outcomes, including responses to the Japanese Orthopaedic Association (JOA) Cervical Myelopathy Evaluation Questionnaire (JOACMEQ), JOA Back Pain Evaluation Questionnaire (JOABPEQ), visual analog scale (VAS), and SF-36 scores. The severity of myelopathy was significantly correlated with each domain of the JOACMEQ and JOABPEQ. There was a negative correlation between the VAS score for each domain and the JOA score. There were significantly positive correlations between the JOA score and the Mental Health, Bodily Pain, Physical Functioning, Role Emotional, and Role Physical domains of the SF-36. One-to-one matching resulted in 50 pairs of patients with OPLL and CS. Although there was no significant between-group difference in scores in any of the domains of the JOACMEQ or JOABPEQ, the VAS scores for pain or numbness in the buttocks or limbs were significantly higher in the CS group; however, there was no marked difference in low back pain, chest tightness, or numbness below the chest between the two study groups. The scores for the Role Physical and Body Pain domains of the SF-36 were significantly higher in the OPLL group than in the CS group, and the mean scores for the other domains was similar between the two groups. The results of this study revealed that patients with OPLL were likely to have neck and low back pain and restriction in ADL. No specific type of pain was found in patients with OPLL when compared with those who had CS.

DOI： 10.1038/s41598-020-62278-3

Open Access

Scopus

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments Reviewed International coauthorship

Takuya Ozawa,Yuichiro Hayashi,Hirohisa Oda, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020.11

Clinical impact of Endoscopic Surgical Skill Qualification System (ESSQS) by Japan Society for Endoscopic Surgery (JSES) for laparoscopic distal gastrectomy and low anterior resection based on the National Clinical Database (NCD) registry. Invited Reviewed Open Access

Akagi T, Endo H, Inomata M, Yamamoto H, Mori T, Kojima K, Kuroyanagi H, Sakai Y, Nakajima K, Shiroshita H, Etoh T, Saida Y, Yamamoto S, Hasegawa H, Ueno H, Kakeji Y, Miyata H, Kitagawa Y, Watanabe M

Annals of gastroenterological surgery Vol. 4 ( 6 ) page： 721 - 734 2020.11

Station number assignment to abdominal lymph node for assisting gastric cancer surgery Reviewed

Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020.10

Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research Invited Reviewed Open Access

Kitaguchi, D; Takeshita, N; Matsuzaki, H; Oda, T; Watanabe, M; Mori, K; Kobayashi, E; Ito, M

INTERNATIONAL JOURNAL OF SURGERY Vol. 79 page： 88 - 94 2020.7

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Surgery

Background: Identifying laparoscopic surgical videos using artificial intelligence (AI) facilitates the automation of several currently time-consuming manual processes, including video analysis, indexing, and video-based skill assessment. This study aimed to construct a large annotated dataset comprising laparoscopic colorectal surgery (LCRS) videos from multiple institutions and evaluate the accuracy of automatic recognition for surgical phase, action, and tool by combining this dataset with AI. Materials and methods: A total of 300 intraoperative videos were collected from 19 high-volume centers. A series of surgical workflows were classified into 9 phases and 3 actions, and the area of 5 tools were assigned by painting. More than 82 million frames were annotated for a phase and action classification task, and 4000 frames were annotated for a tool segmentation task. Of these frames, 80% were used for the training dataset and 20% for the test dataset. A convolutional neural network (CNN) was used to analyze the videos. Intersection over union (IoU) was used as the evaluation metric for tool recognition. Results: The overall accuracies for the automatic surgical phase and action classification task were 81.0% and 83.2%, respectively. The mean IoU for the automatic tool segmentation task for 5 tools was 51.2%. Conclusions: A large annotated dataset of LCRS videos was constructed, and the phase, action, and tool were recognized with high accuracy using AI. Our dataset has potential uses in medical applications such as automatic video indexing and surgical skill assessments. Open research will assist in improving CNN models by making our dataset available in the field of computer vision.

DOI： 10.1016/j.ijsu.2020.05.015

Artificial intelligence for magnifying endoscopy, endocytoscopy, and confocal laser endomicroscopy of the colorectum Invited Reviewed Open Access

Mori, Y; Kudo, SE; Misawa, M; Itoh, H; Oda, M; Mori, K

TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY Vol. 22 ( 2 ) page： 56 - 60 2020.4

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Techniques and Innovations in Gastrointestinal Endoscopy

Because magnifying endoscopy is considered to be more accurate at predicting the histology of colorectal polyps than nonmagnifying endoscopy, it has been attracting a lot of attention, especially in Japan. However, use of magnifying endoscopy is not yet widespread because of its limited availability and the difficulty in interpreting the acquired images. Application of artificial intelligence (AI) is now changing this situation because it helps less-skilled endoscopists to accurately interpret magnified images. Research in this field initially focused on magnifying endoscopy with narrow-band imaging as the target of AI. Most previously published retrospective studies have reported over 90% sensitivity in differentiation of neoplastic lesions; however, automatically indicating the region of interest (ROI) of the polyps that AI should analyze has been found to be challenging. To address this practical problem, some researchers have started to adopt contact endomicroscopy as a target for AI. Contact endomicroscopy includes endocytoscopy (520-fold magnification, Olympus, Tokyo, Japan) and confocal laser endomicroscopy (1000-fold magnification, Mauna Kea, Paris, France). These forms of contact endomicroscopy provide ultramagnified images that make it unnecessary to manually select the ROI because the entire image acquired by contact endomicroscopy is the ROI of the targeted polyps. This strength of contact endomicroscopy has contributed to early implementation of this technology into clinical practice, which may change the utility of magnifying endoscopy in clinical settings and help increase its use globally in the near future.

DOI： 10.1016/j.tgie.2019.150632

Web of Science

Scopus

Clinical application of a surgical navigation system based on virtual thoracoscopy for lung cancer patients: real time visualization of area of lung cancer before induction therapy and optimal resection line for obtaining a safe surgical margin during surgery Invited Reviewed Open Access

Nakamura Shota, Hayashi Yuichiro, Kawaguchi Koji, Fukui Takayuki, Hakiri Shuhei, Ozeki Naoki, Mori Shunsuke, Goto Masaki, Mori Kensaku, Yokoi Kohei

JOURNAL OF THORACIC DISEASE Vol. 12 ( 3 ) page： 672 - 679 2020.3

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Journal of Thoracic Disease

Background: We have developed a surgical navigation system that presents virtual thoracoscopic images using computed tomography (CT) image data, as if you are observing intra-thoracic cavity in synchronization with the real thoracoscopic view. Using this system, we made it possible to simultaneously visualize the ‘area of lung cancer before induction therapy’ and the ‘optimal resection line for obtaining a safe surgical margin’ as a virtual thoracoscopic view. We applied this navigation system in the clinical setting in operations for lung cancer patients with chest wall invasion after induction chemoradiotherapy. Methods: The proposed surgical navigation system consisted of a three-dimensional (3D) positional tracker and a virtual thoracoscopy system. The 3D positional tracker was used to recognize the positional information of the real thoracoscope. The virtual thoracoscopy system generated virtual thoracoscopic views based on CT image data. Combined with these two technologies, patient-to-image registration was performed in two patients, and the results generated a virtual thoracoscopic view that was synchronized with the real thoracoscopic view. Results: The operations were started with video-assisted thoracic surgery (VATS), and the navigation system was activated at the same time. The virtual thoracoscopic view was synchronized with the real thoracoscopic view, which also simultaneously indicated the ‘area of lung cancer before induction therapy’ and the ‘optimal resection lines for obtaining a safe surgical margin’. We marked the optimal lines using an electric scalpel, and then performed lobectomy and chest wall resection with a sufficient surgical margin using these landmarks. Pathological examinations confirmed that the surgical margin was negative. No complications related to the navigation system were encountered during or after the procedures. Conclusions: Using this proposed navigation system, we could obtain a ‘CT-derived virtual intrathoracic 3D view of the patient’ that was aligned with the thoracoscopic view during surgery. The accurate identification of areas of cancer invasion before induction therapy using this system might be a useful for determining optimal surgical resection lines.

DOI： 10.21037/jtd.2019.12.108

Open Access

Web of Science

Scopus

Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation Reviewed International coauthorship Open Access

Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

Medical Image Analysis Vol. 60 page： 101623 2020.2

3Dプリンティングの最新動向 Invited

森健策

インナービジョン Vol. 35 page： 36 - 37 2020

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning Invited Reviewed

Wang P., Shen C., Roth H.R., Yang D., Xu D., Oda M., Misawa K., Chen P.T., Liu K.L., Liao W.C., Wang W., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12444 LNCS page： 192 - 200 2020

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

The performance of deep learning based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.

DOI： 10.1007/978-3-030-60548-3_19

Scopus

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Papers - MORI, Kensaku