Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Diffusion-driven distillation and contrastive learning for class-incremental semantic segmentation of laparoscopic images Invited Reviewed Open Access

inkai Zhao, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.6

Label cleaning and propagation for improved segmentation performance using fully convolutional networks Reviewed

Takaaki Sugino, Yutaro Suzuki, Taichi Kin, Nobuhito Saito, Shinya Onogi, Toshihiro Kawase, Kensaku Mori, Yoshikazu Nakajima

International Journal of Computer Assisted Radiology and Surgery 2021.3

Fully automated segmentation of substantia nigra toward longitudinal analysis of Parkinson’s disease Invited Reviewed Open Access

Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Koji Kamagata, Kei-ichi Ishikawa, Wataru Sako, Nobutaka Hattori, Shigeki Aoki, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.10

GPU-accelerated deformation mapping in hybrid organ models for real-time simulation Invited Reviewed

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.7

Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos Invited Reviewed Open Access

Xinkai Zhao, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.6

Semantic segmentation dataset authoring with simplified labels Invited Reviewed

Leo Uramoto, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.4

Semantic segmentation dataset authoring with simplified labels Invited Reviewed Open Access

Leo Uramoto, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery 2025.4

Predicting Violence Rating Based on Pairwise Comparison. Reviewed Open Access

Ying JI, Yu WANG, Jien KATO, Kensaku MORI

IEICE Transactions on Information and Systems Vol. E103.D ( 12 ) page： 2578 - 2589 2020.12

Computer-aided detection for esophageal achalasia (with video) Invited Reviewed Open Access

Hironari Shiwaku, Masashi Misawa, Haruhiro Inoue, Kai Jiang, Masahiro Oda, Pietro Familiari, Guido Costamagna, Yuto Shimamura, Yuichiro Ikebuchi, Yugo Iwaya, Masaki Ominami, Bu'Hussain Hayee, Khek-Yu Ho, Jimmy B Y So, Hein Myat Thu Htet, Pradeep Bhandari, Kevin Grimes, Helmut Messmann, Bianca Maria Quarta Colosso, Roberta Maselli, Cesare Hassan, Alessandro Repici, Stavros N Stavropoulos, Norio Fukami, Robert Bechara, Michel Kahaleh, Amrita Sethi, Torsten Beyna, Horst Neuhaus, Philip W Y Chiu, Esperanza Grace Santi, Prateek Sharma, Nikolas Eleftheriadis, Hitomi Minami, Gregory Haber, Peter V Draganov, Stefan Seewald, Akio Shiwaku, Yoshiyuki Shiwaku, Kensaku Mori, Shin-Ei Kudo, Suguru Hasegawa

Digestive Endoscopy 2025.6

An artificial intelligence-based diagnostic imaging system with virtual enteroscopy and virtual unfolded views to evaluate small bowel lesions in Crohn's disease. Invited Reviewed Open Access

Furukawa K, Oda M, Watanabe O, Nakamura M, Yamamura T, Maeda K, Mori K, Kawashima H

Revista espanola de enfermedades digestivas Vol. 117 ( 6 ) page： 342 - 343 2025.6

A Deployable Microscopic Image Segmentation Look-Up Table Based on A Dilated CNN Invited Reviewed

Wu Y., Pan J., Kawamura S., Oda M., Hayashi Y., Takebe T., Rueckert D., Mori K.

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 15371 LNCS page： 3 - 13 2025

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

Microscopic image segmentation is indispensable biomedical research. However, accurately segmenting using traditional automatic segmentation methods remains challenging due to fluorescence presenting high intra- and inter-image variability, hindering biomedical analysis. Deep learning (DL)-based methods show remarkable potential in microscopic image segmentation. However, DL models typically require GPU resources, specialized programming skills, and a non-trivial environment setup, making it difficult for medical researchers to use them currently. Therefore, we propose a turnkey solution for microscopic image segmentation by Look-up Table (LUT) which is a data structure used for storing input values and pre-calculated results. Our solution can inherit the segmentation accuracy of DL-based methods and eliminates the need for extensive computational resources, specialized programming, and a complex implementation environment. Specifically, (1) By transferring all potential inputs and their DL model-computed outputs into a LUT, segmentation is achieved through direct output look-up for given pixel values. (2) Additionally, we develop a dilated CNN with adaptive edge padding, enhancing feature learning while meeting LUT building requirements. (3) Moreover, we publicly release a confocal microscopic image dataset and develop a LUT-based plugin (SEG-LUT plugin) for Fiji ImageJ. The experimental results show our framework outperforms other automatic segmentation methods in the Fiji ImageJ. Our solution offers a pathway for easily utilizing DL-based methods for microscopic image segmentation. Our codes, plugin, and dataset are available at https://github.com/MoriLabNU/Microscopic_Image_Segment_LUT.

DOI： 10.1007/978-3-031-77786-8_1

Scopus

Development of real-time navigation system for laparoscopic hepatectomy using magnetic micro sensor. Invited Reviewed

Igami T, Hayashi Y, Yokyama Y, Mori K, Ebata T

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy page： 1 - 11 2024.1

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Minimally Invasive Therapy and Allied Technologies

Background: We report a new real-time navigation system for laparoscopic hepatectomy (LH), which resembles a car navigation system. Material and methods: Virtual three-dimensional liver and body images were reconstructed using the “New-VES” system, which worked as roadmap during surgery. Several points of the patient’s body were registered in virtual images using a magnetic position sensor (MPS). A magnetic transmitter, corresponding to an artificial satellite, was placed about 40 cm above the patient’s body. Another MPS, corresponding to a GPS antenna, was fixed on the handling part of the laparoscope. Fiducial registration error (FRE, an error between real and virtual lengths) was utilized to evaluate the accuracy of this system. Results: Twenty-one patients underwent LH with this system. Mean FRE of the initial five patients was 17.7 mm. Mean FRE of eight patients in whom MDCT was taken using radiological markers for registration of body parts as first improvement, was reduced to 10.2 mm (p =.014). As second improvement, a new MPS as an intraoperative body position sensor was fixed on the right-sided chest wall for automatic correction of postural gap. The preoperative and postoperative mean FREs of 8 patients with both improvements were 11.1 mm and 10.1 mm (p =.250). Conclusions: Our system may provide a promising option that virtually guides LH.

DOI： 10.1080/13645706.2023.2301594

Scopus

PubMed

Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Invited Reviewed

Ueno Y, Oda M, Yamaguchi T, Fukuoka H, Nejima R, Kitaguchi Y, Miyake M, Akiyama M, Miyata K, Kashiwagi K, Maeda N, Shimazaki J, Noma H, Mori K, Oshika T

The British journal of ophthalmology 2024.1

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

Aim To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. Methods This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases. Results The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for’urgent’, 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for’semi-urgent’, 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for’routine’ and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for’observation’, respectively. Conclusions The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.

DOI： 10.1136/bjo-2023-324488

Scopus

PubMed

Adaptive octree cube refinement depending on grasping position for deformable organ models Reviewed

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

Proc.SPIE Vol. 12928 2024

Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery. Invited Reviewed Open Access

Fozilov K, Colan J, Davila A, Misawa K, Qiu J, Hayashi Y, Mori K, Hasegawa Y

Sensors (Basel, Switzerland) Vol. 23 ( 24 ) 2023.12

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

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

DOI： 10.3390/s23249865

Open Access

Scopus

PubMed

Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms. Invited Reviewed

Iwaya M, Hayashi Y, Sakai Y, Yoshizawa A, Iwaya Y, Uehara T, Kitagawa M, Fukayama M, Mori K, Ota H

Gastrointestinal endoscopy Vol. 98 ( 6 ) page： 925 - 933.e1 2023.12

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

Background and Aims: Gastric cancer (GC) is associated with chronic gastritis. To evaluate the risk, the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system was constructed and showed a higher GC risk in stage III or IV patients, determined by the degree of intestinal metaplasia (IM). Although the OLGIM system is useful, evaluating the degree of IM requires substantial experience to produce precise scoring. Whole-slide imaging is becoming routine, but most artificial intelligence (AI) systems in pathology are focused on neoplastic lesions. Methods: Hematoxylin and eosin–stained slides were scanned. Images were divided into each gastric biopsy tissue sample and labeled with an IM score. IM was scored as follows: 0 (no IM), 1 (mild IM), 2 (moderate IM), and 3 (severe IM). Overall, 5753 images were prepared. A deep convolutional neural network (DCNN) model, ResNet50, was used for classification. Results: ResNet50 classified images with and without IM with a sensitivity of 97.7% and specificity of 94.6%. IM scores 2 and 3, involved as criteria of stage III or IV in the OLGIM system, were classified by ResNet50 in 18%. The respective sensitivity and specificity values of classifying IM between scores 0 and 1 and 2 and 3 were 98.5% and 94.9%, respectively. The IM scores classified by pathologists and the AI system were different in only 438 images (7.6%), and we found that ResNet50 tended to miss small foci of IM but successfully identified minimal IM areas that pathologists missed during the review. Conclusions: Our findings suggested that this AI system would contribute to evaluating the risk of GC accuracy, reliability, and repeatability with worldwide standardization.

DOI： 10.1016/j.gie.2023.06.056

Scopus

PubMed

Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification Invited Reviewed

Sadayuki Ito, Hiroaki Nakashima, Toshitaka Yoshii, Satoru Egawa, Kenichiro Sakai, Kazuo Kusano, Shinji Tsutui, Takashi Hirai, Yu Matsukura, Kanichiro Wada, Keiichi Katsumi, Masao Koda, Atsushi Kimura, Takeo Furuya, Satoshi Maki, Narihito Nagoshi, Norihiro Nishida, Yukitaka Nagamoto, Yasushi Oshima, Kei Ando, Masahiko Takahata, Kanji Mori, Hideaki Nakajima, Kazuma Murata, Masayuki Miyagi, Takashi Kaito, Kei Yamada, Tomohiro Banno, Satoshi Kato, Tetsuro Ohba, Satoshi Inami, Shunsuke Fujibayashi, Hiroyuki Katoh, Haruo Kanno, Masahiro Oda, Kensaku Mori, Hiroshi Taneichi, Yoshiharu Kawaguchi, Katsushi Takeshita, Morio Matsumoto, Masashi Yamazaki, Atsushi Okawa, Shiro Imagama

Europian Spine Journal Vol. 32 ( 11 ) page： 3797 - 3806 2023.11

Social Relation Atmosphere Recognition with Relevant Visual Concepts Invited Open Access

JI Ying, WANG Yu, MORI Kensaku, KATO Jien

IEICE Transactions on Information and Systems Vol. E106.D ( 10 ) page： 1638 - 1649 2023.10

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

<p>Social relationships (e.g., couples, opponents) are the foundational part of society. Social relation atmosphere describes the overall interaction environment between social relationships. Discovering social relation atmosphere can help machines better comprehend human behaviors and improve the performance of social intelligent applications. Most existing research mainly focuses on investigating social relationships, while ignoring the social relation atmosphere. Due to the complexity of the expressions in video data and the uncertainty of the social relation atmosphere, it is even difficult to define and evaluate. In this paper, we innovatively analyze the social relation atmosphere in video data. We introduce a Relevant Visual Concept (RVC) from the social relationship recognition task to facilitate social relation atmosphere recognition, because social relationships contain useful information about human interactions and surrounding environments, which are crucial clues for social relation atmosphere recognition. Our approach consists of two main steps: (1) we first generate a group of visual concepts that preserve the inherent social relationship information by utilizing a 3D explanation module; (2) the extracted relevant visual concepts are used to supplement the social relation atmosphere recognition. In addition, we present a new dataset based on the existing Video Social Relation Dataset. Each video is annotated with four kinds of social relation atmosphere attributes and one social relationship. We evaluate the proposed method on our dataset. Experiments with various 3D ConvNets and fusion methods demonstrate that the proposed method can effectively improve recognition accuracy compared to end-to-end ConvNets. The visualization results also indicate that essential information in social relationships can be discovered and used to enhance social relation atmosphere recognition.</p>

DOI： 10.1587/transinf.2023PCP0008

Open Access

Scopus

CiNii Research

Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging. Invited Reviewed Open Access

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

Journal of clinical medicine Vol. 12 ( 15 ) 2023.8

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

Spinal cord tumors are infrequently identified spinal diseases that are often difficult to diagnose even with magnetic resonance imaging (MRI) findings. To minimize the probability of overlooking these tumors and improve diagnostic accuracy, an automatic diagnostic system is needed. We aimed to develop an automated system for detecting and diagnosing spinal schwannomas and meningiomas based on deep learning using You Only Look Once (YOLO) version 4 and MRI. In this retrospective diagnostic accuracy study, the data of 50 patients with spinal schwannomas, 45 patients with meningiomas, and 100 control cases were reviewed, respectively. Sagittal T1-weighted (T1W) and T2-weighted (T2W) images were used for object detection, classification, training, and validation. The object detection and diagnosis system was developed using YOLO version 4. The accuracies of the proposed object detections based on T1W, T2W, and T1W + T2W images were 84.8%, 90.3%, and 93.8%, respectively. The accuracies of the object detection for two spine surgeons were 88.9% and 90.1%, respectively. The accuracies of the proposed diagnoses based on T1W, T2W, and T1W + T2W images were 76.4%, 83.3%, and 84.1%, respectively. The accuracies of the diagnosis for two spine surgeons were 77.4% and 76.1%, respectively. We demonstrated an accurate, automated detection and diagnosis of spinal schwannomas and meningiomas using the developed deep learning-based method based on MRI. This system could be valuable in supporting radiological diagnosis of spinal schwannomas and meningioma, with a potential of reducing the radiologist’s overall workload.

DOI： 10.3390/jcm12155075

Open Access

Scopus

PubMed

Activities of National Institute of Informatics in Japan Invited Reviewed Open Access

Kitsuregawa M., Urushidani S., Yamaji K., Takakura H., Hasuo I., Sato I., Ishikawa F., Echizen I., Mori K.

Communications of the ACM Vol. 66 ( 7 ) page： 58 - 63 2023.6

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Papers - MORI, Kensaku