研究者詳細 - 森　健策

論文 - 森　健策

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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招待有り査読有り

森　健策

インナービジョン 39 巻 ( 7 ) 頁： 6 - 9 2024年7月

Federated 3D multi-organ segmentation with partially labeled and unlabeled dataa 招待有り査読有り Open Access

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

International Journal of Computer Assisted Radiology and Surgery 2024年5月

Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis. 査読有り Open Access

Takahashi H, Ohno E, Furukawa T, Yamao K, Ishikawa T, Mizutani Y, Iida T, Shiratori Y, Oyama S, Koyama J, Mori K, Hayashi Y, Oda M, Suzuki T, Kawashima H

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society 36 巻 ( 4 ) 頁： 463 - 472 2024年4月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Digestive Endoscopy

Objectives: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). Methods: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. Results: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). Conclusion: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.

DOI： 10.1111/den.14622

Scopus

PubMed

CiNii Research

Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT. 招待有り査読有り Open Access

Shen C, Roth HR, Hayashi Y, Oda M, Sato G, Miyamoto T, Rueckert D, Mori K

International journal of computer assisted radiology and surgery 19 巻 ( 4 ) 頁： 655 - 664 2024年4月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: Pancreatic duct dilation is associated with an increased risk of pancreatic cancer, the most lethal malignancy with the lowest 5-year relative survival rate. Automatic segmentation of the dilated pancreatic duct from contrast-enhanced CT scans would facilitate early diagnosis. However, pancreatic duct segmentation poses challenges due to its small anatomical structure and poor contrast in abdominal CT. In this work, we investigate an anatomical attention strategy to address this issue. Methods: Our proposed anatomical attention strategy consists of two steps: pancreas localization and pancreatic duct segmentation. The coarse pancreatic mask segmentation is used to guide the fully convolutional networks (FCNs) to concentrate on the pancreas’ anatomy and disregard unnecessary features. We further apply a multi-scale aggregation scheme to leverage the information from different scales. Moreover, we integrate the tubular structure enhancement as an additional input channel of FCN. Results: We performed extensive experiments on 30 cases of contrast-enhanced abdominal CT volumes. To evaluate the pancreatic duct segmentation performance, we employed four measurements, including the Dice similarity coefficient (DSC), sensitivity, normalized surface distance, and 95 percentile Hausdorff distance. The average DSC achieves 55.7%, surpassing other pancreatic duct segmentation methods on single-phase CT scans only. Conclusions: We proposed an anatomical attention-based strategy for the dilated pancreatic duct segmentation. Our proposed strategy significantly outperforms earlier approaches. The attention mechanism helps to focus on the pancreas region, while the enhancement of the tubular structure enables FCNs to capture the vessel-like structure. The proposed technique might be applied to other tube-like structure segmentation tasks within targeted anatomies.

DOI： 10.1007/s11548-023-03049-z

Open Access

Scopus

PubMed

その他リンク： https://link.springer.com/article/10.1007/s11548-023-03049-z/fulltext.html

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招待有り査読有り

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 123 巻 ( 411 ) 頁： 137 - 140 2024年3月

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招待有り査読有り

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 123 巻 ( 411 ) 頁： 152 - 155 2024年3月

Multi-organ Segmentation from Abdominal CT Volumes based on Self-supervised Learning Method 招待有り査読有り

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

123 巻 ( 411 ) 頁： 127 - 130 2024年3月

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招待有り査読有り

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 123 巻 ( 411 ) 頁： 145 - 148 2024年3月

教師なしモデルベース深度推定を利用した大腸の3次元形状復元招待有り査読有り

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 123 巻 ( 411 ) 頁： 87 - 90 2024年3月

最新の研究動向2023 画像セグメンテーションとMICCAI総括招待有り査読有り

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 123 巻 ( 411 ) 頁： 45 - 49 2024年3月

SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset. 招待有り査読有り国際誌

Zheng T, Oda H, Hayashi Y, Nakamura S, Mori M, Takabatake H, Natori H, Oda M, Mori K

International journal of computer assisted radiology and surgery 19 巻 ( 3 ) 頁： 493 - 506 2024年3月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

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 招待有り査読有り

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年2月

画像情報学の立場からみた脳神経内科とSociet 5.0 招待有り査読有り

森健策

脳神経内科 100 巻 ( 2 ) 頁： 176 - 182 2024年2月

Performance improvement for medical image classification model by using gradient-based analytical feature selection 招待有り査読有り

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

proc.SPIE 2024年2月

Anatomical structure-constrained thrombosis region segmentation and measurement using confocal laser scanning microscopic images 招待有り査読有り

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

proc.SPIE 2024年2月

Continued pretraining for enhanced multi-organ segmentation from CT images 招待有り査読有り

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

proc.SPIE 2024年2月

Intestine segmentation from CT volume based on bidirectional teaching 招待有り査読有り

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

proc.SPIE 2024年2月

Boosting substantia nigra segmentation from T2 weighted MRI via test-time normalization and distance-reweighted loss 招待有り査読有り

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

proc.SPIE 2024年2月

Use of 3D-printed model of liver by experts and novices 招待有り査読有り

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

Current Psychology 2024年2月

人工知能（AI）の最新動向 - RSNA 2023におけるAIトピックス招待有り査読有り

森健策

月刊インナービジョン 2月号 2024年1月

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