Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs. Reviewed Open Access

Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, Oishi R, Miyairi Y, Mori K, Imagama S

BioMed research international Vol. 2023 page： 8495937 2023

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

Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of the thoracic ossification of the posterior longitudinal ligament (OPLL) using deep learning and plain radiography. We retrospectively reviewed the data of 146 patients with thoracic OPLL and 150 control cases without thoracic OPLL. Plain lateral thoracic radiographs were used for object detection, training, and validation. Thereafter, an object detection system was developed, and its accuracy was calculated. The performance of the proposed system was compared with that of two spine surgeons. The accuracy of the proposed object detection model based on plain lateral thoracic radiographs was 83.4%, whereas the accuracies of spine surgeons 1 and 2 were 80.4% and 77.4%, respectively. Our findings indicate that our automated system, which uses a deep learning-based method based on plain radiographs, can accurately detect thoracic OPLL. This system has the potential to improve the diagnostic accuracy of thoracic OPLL.

DOI： 10.1155/2023/8495937

Open Access

Scopus

PubMed

看護師の職域プレゼンティズム（心身不調）をIoTセンシングで早期検出する Invited Reviewed Open Access

山下佳子, 大山慎太郎, 鈴木輝彦, 坂本祐二, 出野義則, 山下暁士, 赤川里美, 藤井晃子, 白鳥義宗, 森健策

生体医工学 Vol. Annual61 ( Abstract ) page： 225_1 - 225_1 2023

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：公益社団法人日本生体医工学会

看護職は心身不調に由来する顕在化していない労働生産性低下状態（プレゼンティズム）が他職種より多く、不規則な勤務形態や強い心身ストレスにより、離職に至る率も高いのが現状である。類似研究は腰痛に注目してウェアラブルデバイスで筋硬度測定によりプレゼンティズム発生を予測するが、性別や運動経験の有無など個人の特性を大きく反映するため予測が難しいと報告がある。そこで本研究では、プレゼンティズムの主な要因で筋骨格系疼痛の原因となる看護行動を、Internet of Things (IoT)センシングで抽出・判別することを目的とする。既設のBLE 屋内測位(AoA法）アンテナを利用した加速度センサデバイスによる看護行動データの収集と、観察者の直接観察による看護行動に対するラベルデータ（看護行動、実施場所、行動の開始・終了時刻を入力）作成を実施した。看護行動は身体プレゼンティズムリスク(腰痛・膝痛・頚肩腕痛)に分類し定義した。1分毎の行動データ（位置情報の平均値、加速度の最大・最小・平均値・標準偏差）、及び時刻を入力データとし、ラベルデータを教師データとして機械学習を行い、行動認識モデルを構築した。看護行動割合は、腰痛43.6%、膝痛3.3%、頚肩腕痛7.4%であった。IoTセンシングで個人ごとのプレゼンティズムに発展する行動をモニタリングすることにより、プレゼンティズムを予防することが可能と示唆された。

DOI： 10.11239/jsmbe.annual61.225_1

Open Access

CiNii Research

膀胱鏡画像におけるtiny-YOLOを用いた腫瘍検出 Invited Reviewed Open Access

牟田口淳, 小田昌宏, 猪口淳一, 森健策, 江藤正俊

生体医工学 Vol. Annual61 ( Abstract ) page： 255_2 - 255_2 2023

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：公益社団法人日本生体医工学会

【背景】膀胱癌は経尿道手術後に再発が多い腫瘍であり、膀胱鏡での腫瘍の見落としが原因とされている。内視鏡での観察は、従来の白色光(WLI)の他に、NBIを使用するが、いずれの腫瘍検出精度は検者の技量・経験に依存するため、検査の再現性・客観性が少ないことが課題である。近年、人工知能(AI)が多くの医療分野で活用されており、AIによる検査は、客観性・再現性を持った上で、エキスパートレベルと同程度の診断能を持つ可能性があるとされている。今回、WLI/NBI膀胱鏡画像を用いて、AIによる腫瘍検出の精度を検証した。【方法】2019年から2021年まで、経尿道的膀胱腫瘍切除術(TURBT)の際に、WLI/NBIを用いて観察を行った症例の手術動画から膀胱鏡画像を作成し、腫瘍を含む画像を腫瘍画像、腫瘍を含まない画像を正常画像と定義した。腫瘍画像内の膀胱腫瘍を矩形でアノテーションを行い、テストデータ用の画像を用いてAIによる感度、特異度、陽性的中率を評価した。AIでの物体検出はtiny-YOLOを用い、腫瘍検出精度の検証を行った。【結果】WLIとNBIから、それぞれ腫瘍画像をそれぞれtiny-YOLOで学習を行い、腫瘍画像(WLI: 525枚、NBI:219枚)と正常画像(WLI:98枚、NBI:108枚)で精度検証を行った。AIによる物体検出の感度/特異度/陽性的中率は、WLIで87.8%/88.8%/97.7%、NBIで82.2%/81.4%/90.0%であった。【結論】膀胱鏡画像において、AIにより比較的良好に腫瘍検出が可能であった。更なる精度改善、リアルタイム検出への課題について、文献的考察を加え報告する。

DOI： 10.11239/jsmbe.annual61.255_2

Open Access

CiNii Research

Surgical Assistance in Laparoscopic Surgery Using AI Invited Reviewed

HAYASHI Yuichiro, MORI Kensaku

Medical Imaging Technology Vol. 40 ( 4 ) page： 164 - 169 2022.9

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

Laparoscopic surgery is currently performed as one of surgical procedures for various organs. Laparoscopic surgery requires highly skill of surgeon. Therefore, computer aided surgery which is used computer technology for assisting surgery has been researched. These assistances are sometimes referred as surgical navigation. In recent year, as the development of AI (Artificial Intelligence) technology, there are many researches on laparoscopic video analysis using deep learning for assisting laparoscopic surgery. In this paper, we introduce our researches about laparoscopic video analysis using AI.

DOI： 10.11409/mit.40.164

CiNii Research

AI for VR Invited Reviewed

森健策

日本VR医学会学術大会プログラム・抄録集 Vol. 2022 ( 0 ) page： 9 - 9 2022.8

BONE MARROW EDEMA SCORE IN HAND X-RAY FILM BY AI DEEP LEARNING ASSOCIATE WITH MRI BONE EDEMA IN RHEUMATOID ARTHRITIS. Invited Reviewed Open Access

Katayama, K; Pan, D; Oda, M; Okubo, T; Mori, K

ANNALS OF THE RHEUMATIC DISEASES Vol. 81 page： 1773 - 1774 2022.6

30年間の医用画像研究経験を振り返り未来を考える Invited Open Access

森健策

情報・システムソサイエティ誌 Vol. 27 ( 1 ) page： 16 - 17 2022.5

Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation 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 Vol. 10 ( 3 ) page： 266 - 273 2022.5

Uncertainty meets 3D-spatial feature in colonoscopic polyp-size determination Invited Reviewed

Hayato Itoh, Masahiro Oda, Kai Jiang, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kenichiro Imai, Sayo Ito, Kinichi Hotta, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Vol. 10 ( 3 ) page： 289 - 298 2022.5

Spatially variant biases considered self-supervised depth estimation based on laparoscopic videos Reviewed

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

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Vol. 10 ( 3 ) page： 274 - 282 2022.5

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Igaki, T; Kitaguchi, D; Kojima, S; Hasegawa, H; Takeshita, N; Mori, K; Kinugasa, Y; Ito, M

DISEASES OF THE COLON & RECTUM Vol. 65 ( 5 ) page： E329 - E333 2022.5

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Diseases of the Colon and Rectum

BACKGROUND: Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an image-guided navigation system with total mesorectal excision. IMPACT OF INNOVATION: The impact of innovation is the development of a deep learning-based image-guided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. TECHNOLOGY, MATERIALS, AND METHODS: This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane was performed. Intraoperative images capturing the total mesorectal excision scene extracted from left colorectal laparoscopic resection videos were used as training data for the deep learning model. Six hundred annotation images were created from 32 videos, with 528 images in the training and 72 images in the test data sets. The experimental feasibility study was conducted at the Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan. Dice coefficient was used to evaluate semantic segmentation accuracy for areolar tissue. PRELIMINARY RESULTS: The developed semantic segmentation model helped locate and highlight the areolar tissue area in the total mesorectal excision plane. The accuracy and generalization performance of deep learning models depend mainly on the quantity and quality of the training data. This study had only 600 images; thus, more images for training are necessary to improve the recognition accuracy. CONCLUSION AND FUTURE DIRECTIONS: We successfully developed a total mesorectal excision plane image-guided navigation system based on an areolar tissue segmentation approach with high accuracy. This may aid surgeons in recognizing the total mesorectal excision plane for dissection.

DOI： 10.1097/DCR.0000000000002393

SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss Reviewed Open Access

Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Takayasu Moriya, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

Journal of Medical Imaging Vol. 9 ( 2 ) page： 024003-1 - 28 2022.4

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study Invited Reviewed

Yasuharu Maeda, Shin-ei Kudo, Noriyuki Ogata, Masashi Misawa, Marietta Iacucci, Mayumi Homma, Tetsuo Nemoto, Kazumi Takishima, Kentaro Mochida, Hideyuki Miyachi, Toshiyuki Baba, Kensaku Mori, Kazuo Ohtsuka, Yuichi Mori

Gastrointestinal Endoscopy 2022.4

Artificial Intelligence for Computer Vision in Surgery A Call for Developing Reporting Guidelines Invited Reviewed Open Access

Kitaguchi, D; Watanabe, Y; Madani, A; Hashimoto, DA; Meireles, OR; Takeshita, N; Mori, K; Ito, M

ANNALS OF SURGERY Vol. 275 ( 4 ) page： E609 - E611 2022.4

A cascaded fully convolutional network framework for dilated pancreatic duct segmentation Invited Reviewed

Chen Shen, Holger R. Roth, Yuichiro Hayashi, Masahiro Oda, Tadaaki Miyamoto, Gen Sato, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 17 ( 2 ) page： 343 - 354 2022.2

impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video) Reviewed

Misaki Ishiyama, Shin-ei Kudo, Masashi Misawa, Yuichi Mori, Yasuharu Maeda, Katsuro Ichimasa, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

Vol. 95 ( 1 ) page： 155 - 163 2022.1

Joint multi organ and tumor segmentation from partial labels using federated learning Invited Reviewed

Chen Shen, Pochuan Wang, Dong Yang, Daguang Xu, Masahiro Oda, Po-Ting Chen, Kao-Lang Liu, Wei-Chin Liao, Chiou-Shann Fuh, Kensaku Mori , Weichung Wang, Holger R. Roth

,LNCS 13573 Vol. 13573 page： 58 - 67 2022

Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes Invited Reviewed

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

Proc. SPIE 12033 Vol. 12033 2022

Depth-based branching level estimation for bronchoscopic navigation Invited Reviewed

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

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( 10 ) page： 1795 - 1804 2021.10

Artificial intelligence and computer-aided diagnosis for colonoscopy: where do we stand now? Invited Reviewed Open Access

Kudo Shin-Ei, Mori Yuichi, Abdel-aal Usama M., Misawa Masashi, Itoh Hayato, Oda Masahiro, Mori Kensaku

TRANSLATIONAL GASTROENTEROLOGY AND HEPATOLOGY Vol. 6 page： 64 2021.10

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Translational Gastroenterology and Hepatology

Computer-aided diagnosis (CAD) for colonoscopy with use of artificial intelligence (AI) is catching increased attention of endoscopists. CAD allows automated detection and pathological prediction, namely optical biopsy, of colorectal polyps during real-time endoscopy, which help endoscopists avoid missing and/or misdiagnosing colorectal lesions. With the increased number of publications in this field and emergence of the AI medical device that have already secured regulatory approval, CAD in colonoscopy is now being implemented into clinical practice. On the other side, drawbacks and weak points of CAD in colonoscopy have not been thoroughly discussed. In this review, we provide an overview of CAD for optical biopsy of colorectal lesions with a particular focus on its clinical applications and limitations.

DOI： 10.21037/tgh.2019.12.14

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Papers - MORI, Kensaku