Research

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.: LPIXEL, collaborated with Dr. Ueda at Osaka City University, developed a deep learning algorithm for detecting cerebral aneurysms. The algorithm was constructed by deep learning with 1271 MRA images including 1477 cerebral aneurysms, and the sensitivity was 92%. The algorithm improved aneurysm detection rate by 5 - 10%, compared with the initial reports.

Research Publication

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms
Author

Daiju Ueda, Akira Yamamoto, Masataka Nishimori, Taro Shimono, Satoshi Doishita, Akitoshi Shimazaki, Yutaka Katayama, Shinya Fukumoto, Antoine Choppin, Yuki Shimahara, Yukio Miki
Journal

Radiology (DOI: 10.1148/radiol.2018180901)
Year

2018

Research Publication

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms
Author

Daiju Ueda, Akira Yamamoto, Masataka Nishimori, Taro Shimono, Satoshi Doishita, Akitoshi Shimazaki, Yutaka Katayama, Shinya Fukumoto, Antoine Choppin, Yuki Shimahara, Yukio Miki
Journal

Radiology (DOI: 10.1148/radiol.2018180901)
Year

2018

Active learning framework with iterative clustering for bioimage classification: LPIXEL, in partnership with Dr. Matsunaga at Tokyo University of Science and Dr. Fujii at National Cancer Center Hospital, developed CARTA, a novel software for automated bioimage classification, applicable to several acquisition equipments. CARTA comprises an active learning algorithm and provides an interactive annotation method. CARTA can support application of various classification schemes depending on the researcher's purpose and classify by some features which are not detected by expert anotaters.

Research Publication

Active learning framework with iterative clustering for bioimage classification
Author

Natsumaro Kutsuna, Takumi Higaki, Sachihiro Matsunaga, Tomoshi Otsuki, Masayuki Yamaguchi, Hirofumi Fujii, Seiichiro Hasezawa
Journal

Nature Communications volume 3, Article number: 1032. DOI: 10.1038/ncomms2030
Year

2012

Research Publication

Active learning framework with iterative clustering for bioimage classification
Author

Natsumaro Kutsuna, Takumi Higaki, Sachihiro Matsunaga, Tomoshi Otsuki, Masayuki Yamaguchi, Hirofumi Fujii, Seiichiro Hasezawa
Journal

Nature Communications volume 3, Article number: 1032. DOI: 10.1038/ncomms2030
Year

2012

The quantification of cells in electron micrographs using deep learning: LPIXEL, jointly with Dr. Sasaki and Dr. Ishiwata of Tokyo Metropolitan Geriatric Institute of Gerontology, conducted a classification analysis using machine learning in order to quantitatively evaluate in electron microscopy images that the ratio of microvilli-expressing cells increased in ABCG+ cell. IMACEL-Classifier was trained with manually collected Microvilli-cell-class and non-microvilli-cell-class cropped images and then applied to ABCG2+ cells and ABCG- cells to quantify the ratio of cell images categorized into each class.

Research Publication

Stemness and anti-cancer drug resistance in ABCG2 highly expressed pancreatic cancer is induced on 3D-culture condition
Author

Norihiko Sasaki, Toshiyuki Ishiwata, Fumio Hasegawa, Masaki Michishita, Hiroki Kawai, Yoko Matsuda, Tomio Arai, Naoshi Ishikawa, Junko Aida, Kaiyo Takubo, Masashi Toyoda
Journal

Cancer Science (DOI: 10.1111/cas.13533)
Year

2018

Research Publication

Stemness and anti-cancer drug resistance in ABCG2 highly expressed pancreatic cancer is induced on 3D-culture condition
Author

Norihiko Sasaki, Toshiyuki Ishiwata, Fumio Hasegawa, Masaki Michishita, Hiroki Kawai, Yoko Matsuda, Tomio Arai, Naoshi Ishikawa, Junko Aida, Kaiyo Takubo, Masashi Toyoda
Journal

Cancer Science (DOI: 10.1111/cas.13533)
Year

2018

Development and application of automatical image aquisition systems for wide-range images with transmitton electron microscopy: LPIXEL, in cooperation with Dr. Toyooka in RIKEN CSRS and Dr. Nagata in Japan Women's University, developed software packages for automated image aquisition and merging microscopic images for wide-range images, exploring methods of specific regions

Research Publication（Left）

Wide‒Range High‒Resolution Transmission Electron Microscopy Reveals Morphological and
Distributional Changes of Endomembrane Compartments during Log to Stationary Transition of
Growth Phase in Tobacco BY‒2 Cells
Author

Kiminori Toyooka, Mayuko Sato, Natsumaro Kutsuna, Takumi Higaki, Fumie Sawaki, Mayumi Wakazaki, Yumi Goto, Seiichiro Hasezawa, Noriko Nagata, Ken Matsuoka
Journal

Plant and Cell Physiology 55: 1544-1555
Year

2014

Research Publication（Left）

Wide‒Range High‒Resolution Transmission Electron Microscopy Reveals Morphological and
Distributional Changes of Endomembrane Compartments during Log to Stationary Transition of
Growth Phase in Tobacco BY‒2 Cells
Author

Kiminori Toyooka, Mayuko Sato, Natsumaro Kutsuna, Takumi Higaki, Fumie Sawaki, Mayumi Wakazaki, Yumi Goto, Seiichiro Hasezawa, Noriko Nagata, Ken Matsuoka
Journal

Plant and Cell Physiology 55: 1544-1555
Year

2014

Growth analysis and the form and traits quantification of rice plant: LPIXEL developed image processing techniques and image aquisition methods for quantitative analysis of development process in rice plant, together with Associate Dr. Sugita(Konishi) at Kagawa University.

Research Publication

Growth analysis and the form and traits quantification of rice plant
Author

朽名夏麿, 川田亮太, 杉田(小西)左江子, 馳澤盛一郎
Journal

日本植物学会第78回大会
Year

2014

Research Publication

Growth analysis and the form and traits quantification of rice plant
Author

朽名夏麿, 川田亮太, 杉田(小西)左江子, 馳澤盛一郎
Journal

日本植物学会第78回大会
Year

2014