著者

原田 康平 金 亨燮 タン ジュークイ 石川 聖二 山村 雄太郎 山本 晃義

出版者

日本医用画像工学会

雑誌

Medical Imaging Technology (ISSN:0288450X)

巻号頁・発行日

vol.26, no.4, pp.246, 2008 (Released:2016-02-17)

著者

芳野 由利子 陸 慧敏 金 亨燮 村上 誠一 青木 隆敏 木戸 尚治

出版者

医用画像情報学会

雑誌

医用画像情報学会雑誌 (ISSN:09101543)

巻号頁・発行日

vol.36, no.2, pp.77-82, 2019-06-30 (Released:2019-06-28)

参考文献数

14

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A temporal subtraction image is obtained by subtracting a previous image, which are warped to match between the structures of the previous image and one of a current image, from the current image. The temporal subtraction technique removes normal structures and enhances interval changes such as new lesions and changes of existing abnormalities from a medical image. However, many artifacts remain on a temporal subtraction image and these can be detected as false positives on the subtraction images. In this paper, we propose a 3D-CNN after initial nodule candidates are detected using temporal subtraction technique. To compare the proposed 3D-CNN, we used 7 model architectures, which are 3D ShallowNet, 3D-AlexNet, 3D-VGG11, 3D-VGG13, 3D-ResNet8, 3D-ResNet20, 3D-ResNet32, with these performance on 28 thoracic MDCT cases including 28 small-sized lung nodules. The higher performance is showed on 3D-AlexNet.