Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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AI搭載内視鏡画像診断支援プログラム (EndoBRAINシリーズ) の開発から保険収載までの道程 Invited Reviewed

森　健策

インナービジョン Vol. 39 ( 7 ) page： 6 - 9 2024.7

Federated 3D multi-organ segmentation with partially labeled and unlabeled dataa Invited Reviewed

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

International Journal of Computer Assisted Radiology and Surgery 2024.5

不確実性を用いた胸部CT画像からのCOVID-19病変セグメンテーション手法 Invited Reviewed

楊天予, 小田昌宏, 鄭通, 林雄一郎, 森健策

電子情報通信学会技術研究報告(MI), 信学技報 Vol. 123 ( 411 ) page： 137 - 140 2024.3

血管強調画像とAutoMLを用いた腹部CT像からの多臓器領域抽出 Invited Reviewed

大野真奈, 申忱, Holger R. Roth, 小田昌宏, 林雄一郎, 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. 123 ( 411 ) page： 152 - 155 2024.3

Multi-organ Segmentation from Abdominal CT Volumes based on Self-supervised Learning Method Invited Reviewed

Yaqi Yang, Chen Shen, Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

Vol. 123 ( 411 ) page： 127 - 130 2024.3

面状構造強調に基づく2D U-Netを用いた肺マイクロCT像からの肺胞セグメンテーション Invited Reviewed

深井大輔, 小田紘久, 林雄一郎, 鄭通, 中村彰太, 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. 123 ( 411 ) page： 145 - 148 2024.3

教師なしモデルベース深度推定を利用した大腸の3次元形状復元 Invited Reviewed

小野坂捺, 伊東隼人, 小田昌宏, 三澤将史, 森悠一, 工藤進英, 森健策

電子情報通信学会技術研究報告(MI), 信学技報 Vol. 123 ( 411 ) page： 87 - 90 2024.3

最新の研究動向2023 画像セグメンテーションとMICCAI総括 Invited Reviewed

小田昌宏, 音丸格, 古川亮, 森健策

電子情報通信学会技術研究報告(MI), 信学技報 Vol. 123 ( 411 ) page： 45 - 49 2024.3

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

International Journal of Computer Assisted Radiology and Surgery Invited Reviewed

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

Enhanced self-supervised monocular depth estimation with self-attention and joint depth-pose loss for laparoscopic images 2024.2

画像情報学の立場からみた脳神経内科とSociet 5.0 Invited Reviewed

森健策

脳神経内科 Vol. 100 ( 2 ) page： 176 - 182 2024.2

Performance improvement for medical image classification model by using gradient-based analytical feature selection Invited Reviewed

Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

proc.SPIE 2024.2

Anatomical structure-constrained thrombosis region segmentation and measurement using confocal laser scanning microscopic images Invited Reviewed

Cheng Wang, Yunheng Wu, Yuichiro Hayashi, Masahiro Oda, Shuntaro Kawamura, Takanori Takebe, Kensaku Mori

proc.SPIE 2024.2

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

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

Proc.SPIE 2024.2

Continued pretraining for enhanced multi-organ segmentation from CT images Invited Reviewed

Yaqi Yang, Chen Shen, Yucheng Tang, Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

proc.SPIE 2024.2

Intestine segmentation from CT volume based on bidirectional teaching Invited Reviewed

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

proc.SPIE 2024.2

Boosting substantia nigra segmentation from T2 weighted MRI via test-time normalization and distance-reweighted loss Invited Reviewed

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

proc.SPIE 2024.2

Use of 3D-printed model of liver by experts and novices Invited Reviewed

Akihiro Maehigashi, Kazuhisa Miwa, Masahiro Oda, Yoshihiko Nakamura, Kensaku Mori, Tsuyoshi Igami

Current Psychology 2024.2

人工知能（AI）の最新動向 - RSNA 2023におけるAIトピックス Invited Reviewed

森健策

月刊インナービジョン 2月号 2024.1

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

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Papers - MORI, Kensaku