キクチ　マサユキ   MASAYUKI KIKUCHI   菊池　眞之    所属   コンピュータサイエンス学部 コンピュータサイエンス学科    職種   准教授
言語種別	英語
発行・発表の年月	2019/09
形態種別	学術論文
査読	査読あり
標題	Effectiveness of Pseudo 3D Feature Learning for Spinal Segmentation by CNN with U-Net Architecture
執筆形態	共著
掲載誌名	Journal of Image and Graphics
掲載区分	国外
出版社・発行元	Journal of Image and Graphics
巻・号・頁	7(3),pp.107-111
総ページ数	5
著者・共著者	Naofumi Shigeta, Mikoto Kamata, Masayuki Kikuchi
概要	In the medical field, automatic extraction of spinal region from CT images has been desired. Among various methods for image segmentation, one of the convolutional neural network models called U-Net [1] has been shown to attain good performance with small data set size. Previous study by Kamata et al. [2] applied U-Net for spine segmentation task and achieved 82.7% accuracy for unlearned CT images. However, the method had difficulty in the precision of the 3D shape. This study attempted extraction of spine region with higher precision by adopting pseudo 3D feature learning for U-Net.
外部リンクURL	http://www.joig.org/index.php?m=content&c=index&a=show&catid=52&id=218