Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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A study on improvement of airway segmentation using Hybrid method Invited Reviewed

Qier, M; Kitasaka, T; Nimura, Y; Oda, M; Mori, K

PROCEEDINGS 3RD IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION ACPR 2015 page： 549 - 553 2015

Connection method of separated luminal regions of intestine from CT volumes Invited Reviewed

Oda Masahiro, Kitasaka Takayuki, Furukawa Kazuhiro, Watanabe Osamu, Ando Takafumi, Hirooka Yoshiki, Goto Hidemi, Mori Kensaku

MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS Vol. 9414 2015

Development of a new detection device using a glass clip emitting infrared fluorescence for laparoscopic surgery of gastric cancer Invited Reviewed

Inada Shunko Albano, Fuchi Shingo, Mori Kensaku, Hasegawa Junichi, Misawa Kazunari, Nakanishi Hayao

6TH INTERNATIONAL CONFERENCE ON OPTICAL, OPTOELECTRONIC AND PHOTONIC MATERIALS AND APPLICATIONS (ICOOPMA) 2014 Vol. 619 2015

Development and clinical application of surgical navigation system for laparoscopic hepatectomy Invited Reviewed

Hayashi Yuichiro, Igami Tsuyoshi, Hirose Tomoaki, Nagino Masato, Mori Kensaku

MEDICAL IMAGING 2015: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING Vol. 9415 2015

A clinical assessment of three-dimensional-printed liver model navigation for thrice or more repeated hepatectomy based on a conversation analysis Invited Reviewed Open Access

Igami, T., Maehigashi, A., Nakamura, Y., Hayashi, Y., Oda, M., Yokoyama, Y., Mizuno, T., Yamaguchi, J., Onoe, S., Sunagawa, M., Watanabe, N., Baba, T., Kawakatsu, S., Mori, K., Miwa, K., Ebata, T.

Surgery Today Vol. 54 ( 10 ) page： 1238 - 1247 2024.10

SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset. Invited Reviewed International journal

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

International journal of computer assisted radiology and surgery Vol. 19 ( 3 ) page： 493 - 506 2024.3

Language：English Publishing type：Research paper (scientific journal)

PURPOSE: We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural network (DCN). However, LR-HR images in medical imaging are commonly acquired from different imaging devices, and acquiring LR-HR image pairs needs registration. Registered LR-HR images have registration errors inevitably. Using LR-HR images with registration error for training an SR DCN causes collapsed SR results. To address these challenges, we introduce a novel SR approach designed specifically for registered LR-HR medical images. METHODS: We propose style-subnets-assisted generative latent bank for large-factor super-resolution (SGSR) trained with registered medical image datasets. Pre-trained generative models named generative latent bank (GLB), which stores rich image priors, can be applied in SR to generate realistic and faithful images. We improve GLB by newly introducing style-subnets-assisted GLB (S-GLB). We also propose a novel inter-uncertainty loss to boost our method's performance. Introducing more spatial information by inputting adjacent slices further improved the results. RESULTS: SGSR outperforms state-of-the-art (SOTA) supervised SR methods qualitatively and quantitatively on multiple datasets. SGSR achieved higher reconstruction accuracy than recently supervised baselines by increasing peak signal-to-noise ratio from 32.628 to 34.206 dB. CONCLUSION: SGSR performs large-factor SR while given a registered LR-HR medical image dataset with registration error for training. SGSR's results have both realistic textures and accurate anatomical structures due to favorable quantitative and qualitative results. Experiments on multiple datasets demonstrated SGSR's superiority over other SOTA methods. SR medical images generated by SGSR are expected to improve the accuracy of pre-surgery diagnosis and reduce patient burden.

DOI： 10.1007/s11548-023-03037-3

Scopus

PubMed

YOLOv7-RepFPN: Improving real-time performance of laparoscopic tool detection on embedded systems Invited Reviewed

Yuzhang Liu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

Healthcare Technology Letters 2024

Language：English Publishing type：Research paper (scientific journal)

This study focuses on enhancing the inference speed of laparoscopic tool detection on embedded devices. Laparoscopy, a minimally invasive surgery technique, markedly reduces patient recovery times and postoperative complications. Real-time laparoscopic tool detection helps assisting laparoscopy by providing information for surgical navigation, and its implementation on embedded devices is gaining interest due to the portability, network independence and scalability of the devices. However, embedded devices often face computation resource limitations, potentially hindering inference speed. To mitigate this concern, the work introduces a two-fold modification to the YOLOv7 model: the feature channels and integrate RepBlock is halved, yielding the YOLOv7-RepFPN model. This configuration leads to a significant reduction in computational complexity. Additionally, the focal EIoU (efficient intersection of union) loss function is employed for bounding box regression. Experimental results on an embedded device demonstrate that for frame-by-frame laparoscopic tool detection, the proposed YOLOv7-RepFPN achieved an mAP of 88.2% (with IoU set to 0.5) on a custom dataset based on EndoVis17, and an inference speed of 62.9 FPS. Contrasting with the original YOLOv7, which garnered an 89.3% mAP and 41.8 FPS under identical conditions, the methodology enhances the speed by 21.1 FPS while maintaining detection accuracy. This emphasizes the effectiveness of the work.

DOI： 10.1049/htl2.12072

Scopus

Towards better laparoscopic video segmentation: A class-wise contrastive learning approach with multi-scale feature extraction Invited Reviewed

Luyang Zhang, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

Healthcare Technology Letters 2024

Language：English Publishing type：Research paper (scientific journal)

The task of segmentation is integral to computer-aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image-level representations from unlabelled data. This study leverages classification labels to enhance the accuracy of the segmentation model trained on limited annotated data. The method uses a multi-scale projection head to extract image features at various scales. The partitioning method for positive sample pairs is then improved to perform contrastive learning on the extracted features at each scale to effectively represent the differences between positive and negative samples in contrastive learning. Furthermore, the model is trained simultaneously with both segmentation labels and classification labels. This enables the model to extract features more effectively from each segmentation target class and further accelerates the convergence speed. The method was validated using the publicly available CholecSeg8k dataset for comprehensive abdominal cavity surgical segmentation. Compared to select existing methods, the proposed approach significantly enhances segmentation performance, even with a small labelled subset (1–10%) of the dataset, showcasing a superior intersection over union (IoU) score.

DOI： 10.1049/htl2.12069

Scopus

Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations Invited Reviewed

Zhou Zheng, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

Healthcare Technology Letters 2024

Language：English Publishing type：Research paper (scientific journal)

This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large-scale dataset for centralized learning is usually impeded due to data silos and privacy issues. Besides, local clients, such as hospitals or medical institutes, may hold datasets with diverse and imperfect annotations. These datasets can include scarce annotations (many samples are unlabelled), noisy labels prone to errors, and scribble annotations with less precision. Federated learning (FL) has emerged as an attractive paradigm for developing global models with these locally distributed datasets. However, its potential in instrument segmentation has yet to be fully investigated. Moreover, the problem of learning from various imperfect annotations in an FL setup is rarely studied, even though it presents a more practical and beneficial scenario. This work rethinks instrument segmentation in such a setting and propose a practical FL framework for this issue. Notably, this approach surpassed centralized learning under various imperfect annotation settings. This method established a foundational benchmark, and future work can build upon it by considering each client owning various annotations and aligning closer with real-world complexities.

DOI： 10.1049/htl2.12068

Scopus

Artificial intelligence in a prediction model for post-ERCP pancreatitis Reviewed

Takahashi Hidekazu, Eizaburo Ohno, Taiki Furukawa, Kentaro Yamao, Takuya Ishikawa, Yasuyuki Mizutani, Tadashi Iida, Yoshimune Shiratori, Shintaro Oyama, Junji Koyama, Kensaku Mori, Yuichiro Hayashi, Masahiro Oda, Takahisa Suzuki, Hiroki Kawashima

Digestive Endoscopy 2023.7

DEVELOPMENT OF A MACHINE-LEARNING MODEL FOR PREDICTING POST-ERCP PANCREATITIS Invited Reviewed

Gastrointestinal Endoscopy Vol. 97 ( 6 ) page： AB656 - AB656 2023.6

Development of panorama vision ring for thoracoscopy. Invited Reviewed International journal

Takayuki Kitasaka, Shota Nakamura, Yuichiro Hayashi, Tsuyoshi Nakai, Yasuhiro Nakai, Kensaku Mori, Toyofumi Fengshi Chen-Yoshikawa

International journal of computer assisted radiology and surgery Vol. 18 ( 5 ) page： 945 - 952 2023.5

Language：English Publishing type：Research paper (scientific journal)

PURPOSE: Minimally invasive surgery (MIS) using a thoraco- or laparoscope is becoming a more common surgical technique. In MIS, a magnified view from a thoracoscope helps surgeons conduct precise operations. However, there is a risk of the visible area becoming narrow. To confirm that the operation field is safe, the surgeon will draw the thoracoscope back to check the marginal area of the target and insert it again many times during MIS. To reduce the surgeon's load, we aim to visualize the entire thoracic cavity using a newly developed device called "panorama vision ring" (PVR). METHOD: The PVR is used instead of a wound retractor or a trocar. It is a ring-type socket with one big hole for the thoracoscope and four small holes for tiny cameras placed around the big hole. The views from the tiny cameras are fused into one wider view that visualizes the entire thoracic cavity. A surgeon can proceed with an operation by checking what exists outside of the thoracoscopic view. Also, she/he can check whether or not bleeding has occurred from the image of the entire cavity. RESULTS: We evaluated the view-expansion ability of the PVR by using a three-dimensional full-scale thoracic model. The experimental results showed that the entire thoracic cavity could be visible in a panoramic view generated by the PVR. We also demonstrated pulmonary lobectomy in virtual MIS using the PVR. Surgeons could perform a pulmonary lobectomy while checking the entire cavity. CONCLUSION: We developed the PVR, which uses tiny auxiliary cameras to create a panoramic view of the entire thoracic cavity during MIS. We aim to make MIS safer for patients and more comfortable for surgeons through the development of the PVR.

DOI： 10.1007/s11548-023-02859-5

Scopus

PubMed

CT像からの腸閉塞検出における中心線を用いた後処理手法

陳思睿, 小田紘久, 安芹, 林雄一郎, 北坂孝幸, 滝本愛太朗, 檜顕成, 内田広夫, 鈴木耕次郎, 小田昌宏, 森健策

電子情報通信学会技術研究報告(MI)信学技報 Vol. 122 ( 417 ) page： 223 - 228 2023.3

医用画像とAI Reviewed

カレントテラピー Vol. 41 ( 39 ) page： 79 - 79 2023.3

Comparative study of the Small Intestine segmentation based on 2D and 3D U-Nets

Qin An, Hirohisa Oda, Sirui Chen, Yuichiro Hayashi, Takayuki Kitasaka, Hiroo Uchida, Akinari Hinoki, Kojiro Suzuki, Aitaro Takimoto, Masahiro Oda, Kensaku Mori

Vol. 122 ( 417 ) page： 46 - 51 2023.3

Average Templateを使ったCT像からのCOVID-19異常陰影領域セグメンテーション手法

柳凱, 小田昌宏, 鄭通, 林雄一郎, 大竹義人, 橋本正弘, 明石敏昭, 青木茂樹, 森　健策

電子情報通信学会技術研究報告(MI) 信学技報 Vol. 122 ( 47 ) page： 40 - 45 2023.3

術前画像情報を用いた腹腔鏡映像からの血管位置予測の検討

榎本圭吾, 林雄一郎, 北坂孝幸, 小田昌宏, 三澤一成, 森健策

電子情報通信学会技術研究報告(MI) 信学技報 Vol. 122 ( 417 ) page： 63 - 68 2023.3

グラフニューラルネットワークを用いた血管名自動命名における臓器特徴の有効性の調査

出口智也, 林雄一郎, 北坂孝幸, 小田昌宏, 三澤一成, 森健策

電子情報通信学会技術研究報告(MI), 信学技報 Vol. 122 ( 417 ) page： 105 - 110 2023.3

L-former : a lightweight transformer for realistic medical image generation and its application to super-resolution Reviewed

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

proc. SPIE 2023.2

Priority attention network with Bayesian learning for fully automatic segmentation of substantia nigra from neuromelanin MRI Invited Reviewed

Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kensaku Mori

Proc.SPIE12464 2023.2

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Papers - MORI, Kensaku