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Committee Date Time Place Paper Title / Authors Abstract Paper #
MICT, MI 2022-11-18
14:25
Aichi Nagoya Institute of Technology Development of a Statistical Model for Predicting Aging Change in Spine and Pelvis Based on Landmarks Detected in a Large Scale Torso CT Image Database
Yuga Shimomoto, Yoshito Otake, Tomoki Hakotani, Mazen Soufi (NAIST), Hideki Shigematu (Nara Med. Univ.), Keisuke Uemura (Osaka Univ.), Masaki Takao (Ehime Univ.), Toshiaki Akashi (Juntendo Univ.), Kensaku Mori (Nagoya Univ./NII), Kento Aida (NII), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MICT2022-39 MI2022-68
One way to describe variations in skeletal shape is a statistical shape model (SSM), which statistically analyzes organ ... [more] MICT2022-39 MI2022-68
pp.29-32
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