研究者詳細 - 森　健策

論文 - 森　健策

分割表示 1,772 件中 301 - 320 件目／全件表示 >>

Improved visual SLAM for bronchoscope tracking and registration with pre-operative CT images

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

Medical Imaging 2020: Image Processing 11315 巻 2020年2月

CycleGANによる腸管電子洗浄とその腸管閉塞部位検出への応用

西尾光平, 小田紘久, 千馬耕亮, 北坂孝幸, 林雄一郎, 伊東隼人, 小田昌宏, 檜顕成, 内田広夫, 森健策

電子情報通信学会技術研究報告(MI) 119 巻 ( 399 ) 頁： 243 - 248 2020年1月

臨床肺CT画像と切除肺マイクロCT画像の非剛体位置合わせ手法の検討

波多腰慎矢, 小田紘久, 林雄一郎, Holger R. Roth, 中村彰太, 小田昌宏, 森雅樹, 高畠博嗣, 名取博, 森健策

電子情報通信学会技術研究報告(MI) 119 巻 ( 399 ) 頁： 249 - 254 2020年1月

MICCAI 2019参加報告

小田昌宏, 伊東隼人, 宮内翔子, 諸岡健一, 松崎博貴, 花岡昇平, 古川亮, 増谷佳孝, 森健策

電子情報通信学会技術研究報告(MI) 119 巻 ( 399 ) 頁： 219 - 226 2020年1月

仮想腹腔鏡画像生成と深層学習による腹腔鏡画像からの術具領域セグメンテーション

小澤卓也, 林雄一郎, 小田紘久, 小田昌宏, 北坂孝幸, 竹下修由, 伊藤雅昭, 森健策

電子情報通信学会技術研究報告(MI) 119 巻 ( 399 ) 頁： 129 - 134 2020年1月

腹腔鏡下手術支援のためのU-Netに基づく腹腔鏡動画像からの出血領域の推定

山本翔太, 林雄一郎, 盛満慎太郎, 小澤卓也, 北坂孝幸, 小田昌宏, 竹下修由, 伊藤雅昭, 森健策

電子情報通信学会技術研究報告(MI) 119 巻 ( 399 ) 頁： 209 - 214 2020年1月

Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset 国際誌

Itoh, H; Roth, H; Oda, M; Misawa, M; Mori, Y; Kudo, SE; Mori, K

HEALTHCARE TECHNOLOGY LETTERS 6 巻 ( 6 ) 頁： 237 - 242 2019年12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：Healthcare Technology Letters

This Letter presents a stable polyp-scene classification method with low false positive (FP) detection. Precise automated polyp detection during colonoscopies is essential for preventing colon-cancer deaths. There is, therefore, a demand for a computer-assisted diagnosis (CAD) system for colonoscopies to assist colonoscopists. A high-performance CAD system with spatiotemporal feature extraction via a three-dimensional convolutional neural network (3D CNN) with a limited dataset achieved about 80% detection accuracy in actual colonoscopic videos. Consequently, further improvement of a 3D CNN with larger training data is feasible. However, the ratio between polyp and non-polyp scenes is quite imbalanced in a large colonoscopic video dataset. This imbalance leads to unstable polyp detection. To circumvent this, the authors propose an efficient and balanced learning technique for deep residual learning. The authors’ method randomly selects a subset of non-polyp scenes whose number is the same number of still images of polyp scenes at the beginning of each epoch of learning. Furthermore, they introduce post-processing for stable polyp-scene classification. This post-processing reduces the FPs that occur in the practical application of polyp-scene classification. They evaluate several residual networks with a large polyp-detection dataset consisting of 1027 colonoscopic videos. In the scene-level evaluation, their proposed method achieves stable polyp-scene classification with 0.86 sensitivity and 0.97 specificity.

DOI： 10.1049/htl.2019.0079

Realistic endoscopic image generation method using virtual-to-real image-domain translation

Oda, M; Tanaka, K; Takabatake, H; Mori, M; Natori, H; Mori, K

HEALTHCARE TECHNOLOGY LETTERS 6 巻 ( 6 ) 頁： 214 - 219 2019年12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：Healthcare Technology Letters

A realistic image generation method for visualisation in endoscopic simulation systems is proposed in this study. Endoscopic diagnosis and treatment are performed in many hospitals. To reduce complications related to endoscope insertions, endoscopic simulation systems are used for training or rehearsal of endoscope insertions. However, current simulation systems generate non-realistic virtual endoscopic images. To improve the value of the simulation systems, improvement of the reality of their generated images is necessary. The authors propose a realistic image generation method for endoscopic simulation systems. Virtual endoscopic images are generated by using a volume rendering method from a CT volume of a patient. They improve the reality of the virtual endoscopic images using a virtual-to-real image-domain translation technique. The image-domain translator is implemented as a fully convolutional network (FCN). They train the FCN by minimising a cycle consistency loss function. The FCN is trained using unpaired virtual and real endoscopic images. To obtain high-quality image-domain translation results, they perform an image cleansing to the real endoscopic image set. They tested to use the shallow U-Net, U-Net, deep U-Net, and U-Net having residual units as the image-domain translator. The deep U-Net and U-Net having residual units generated quite realistic images.

DOI： 10.1049/htl.2019.0071

Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation

Masahiro Oda, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Healthcare Technology Letters 6 巻 ( 6 ) 頁： 214-219 2019年12月

Generative Adversarial Networks Showcase: Their Mechanisms and Radiological Applications

Masahiro Oda, Hirohisa Oda, Kanako K. Kumamaru, Shigeki Aoki, Hiroshi Natori, Kensaku Mori, Masaki Mori, Hirotsugu Takabatake

RSNA2019 頁： AI020-EB-X 2019年12月

Automatic Quantitative Analysis of Kidney Tumor Using 3D Fully Convolutional Network

Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Naoto Sassa, Tokunori Yamamoto, Kensaku Mori

RSNA2019 頁： UR002-EB-X 2019年12月

Technique for Improving Accuracy of Deep Learning-based Multi-Organ Segmentation from CT Volumes

Chen Shen, Hirohisa Oda, MENG, Masahiro Oda, Holger R. Roth, Yuichiro Hayashi, Kensaku Mori, Takayuki Kitasaka, Kazunari Misawa

RSNA2019 頁： N021-EC-X 2019年12月

Micro Lung Cancer Analysis Based on Micro CT Imaging Using Generative Adversarial Network

Kensaku Mori, Takayasu Moriya, Hirohisa Oda, MENG , Midori Mitarai, Masahiro Oda, Shota Nakamura, Takaaki Sugino, Holger R. Roth

RSNA2019 頁： CH007-EC-X 2019年12月

大腸肛門病理学におけるAI利用の将来像

森健策

日本大腸肛門病学学術集会　抄録号頁： A17 2019年10月

機械学習を用いた医療支援

森健策

第84回日本泌尿器気学会東部総会, プログラム抄録集頁： 123 2019年10月

胸部領域AIの歴史と今後-歴史的研究を振り返りながら今後を展望する-

森健策

臨床画像 35 巻 ( 10 ) 頁： 1139-1149 2019年10月

Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

Takayasu Moriya, Hirohisa Oda, Midori Mitarai, Shota Nakamura, Holger R. Roth, Masahiro Oda, Kensaku Mori

MICCAI 2019 LNCS 11769 巻頁： 240-248 2019年10月

Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical images

Chenglong Wang, Yuichiro Hayashi, Masahiro Oda, Hayato Itoh, Takayuki Kitasaka, Alejandro Frangi, Kensaku Mori

MICCAI 2019 LNCS 11769 巻頁： 348-356 2019年10月

Intelligent Image Synthesis to Attack a egmentation CNN Using Adversarial Learning

Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

MICCAI 2019 LNCS 11827 巻頁： 90-99 2019年10月

Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis

Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Benjamin Villard, Hizuru Amano, Kosuke Chiba, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

MICCAI 2019 LNCS 11840 巻頁： 104-114 2019年10月

　前のページ - 次のページ　

<森 健策>

論文 - 森 健策

<森　健策>

論文 - 森　健策