須鎗弘樹

研究者氏名	須鎗弘樹
	スヤリヒロキ
URL	https://sites.google.com/view/hirokisuyari/
所属	千葉大学
部署	大学院工学研究院総合工学講座
職名	教授
学位	理学士(東京理科大学), 理学修士(東京理科大学), 博士(理学)(東京理科大学)
J-Global ID	200901046263265090

研究キーワード

エントロピー ,複雑系 ,情報理論

研究分野

情報通信 / 情報学基礎論 /

経歴

2013年4月

現在

千葉大学工学部教授

2008年4月

2013年3月

千葉大学工学部情報画像学科准教授

2000年4月

2008年3月

千葉大学工学部情報画像工学科助教授

論文

12 3 4 5 >

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Manami Takahashi Reika Kosuda Hiroyuki Takaoka Hajime Yokota Yasukuni Mori Joji Ota Takuro Horikoshi Yasuhiko Tachibana Hideki Kitahara Masafumi Sugawara Tomonori Kanaeda Hiroki Suyari Takashi Uno Yoshio Kobayashi

Heart and vessels 38(11) 1318-1328 2023年8月 [査読有り]

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic ac...

Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy

Ryusuke Hirai Shinichiro Mori Hiroki Suyari Hiroshi Tsuji Hitoshi Ishikawa

Scientific Reports 13(1) 2023年5月 [査読有り]

Abstract

To perform setup procedures including both positional and dosimetric information, we developed a CT–CT rigid image registration algorithm utilizing water equivalent pathlength (WEPL)-based image registration and compared the resulting do...

Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment

Toshio Kumakiri Shinichiro Mori Yasukuni Mori Ryusuke Hirai Ayato Hashimoto Yasuhiko Tachibana Hiroki Suyari Hitoshi Ishikawa

Physical and Engineering Sciences in Medicine 2023年3月 [査読有り]

Spatiotemporal forecasting of vertical track alignment with exogenous factors

Katsuya Kosukegawa Yasukuni Mori Hiroki Suyari Kazuhiko Kawamoto

Scientific Reports 13(1) 2354-2354 2023年2月 [査読有り]

Abstract

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial cor...

Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study

Yuki Terasaki Hajime Yokota Kohei Tashiro Takuma Maejima Takashi Takeuchi Ryuna Kurosawa Shoma Yamauchi Akiyo Takada Hiroki Mukai Kenji Ohira Joji Ota Takuro Horikoshi Yasukuni Mori Takashi Uno Hiroki Suyari

Frontiers in Neurology 12 742126-742126 2022年1月 [査読有り]

Current deep learning-based cerebral aneurysm detection demonstrates high sensitivity, but produces numerous false-positives (FPs), which hampers clinical application of automated detection systems for time-of-flight magnetic resonance angiography...