- 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
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.