Suh, Sungho Cheon, Sojeong Lee, Yong Oh Lee, Deukhee Chang, Dong-Jin 2024-01-19T08:38:34Z 2024-01-19T08:38:34Z 2022-02-26 2021-09 0302-9743 https://pubs.kist.re.kr/handle/201004/113324 Synthetic CT images are used in data augmentation methods to tackle small and fragmented training datasets in medical imaging. Three-dimensional conditional generative adversarial networks generate lung nodule synthesis, controlling malignancy and benignancy. However, the synthesis still has limitations, such as spatial discontinuity, background changes, and vast computational cost. We propose a novel CT generation model using attribute-guided generative adversarial networks. The proposed model can generate 2D synthetic slices sequentially with U-Net architecture and bi-directional convolutional long short-term memory for nodule reconstruction and injection. Nodule feature information is considered as input in the latent space in U-Net to generate targeted synthetic nodules. The benchmark with LIDC-IDRI dataset showed that the lung nodule synthesis quality is comparable to 3D generative models in the Visual Turing test with lower computation costs. English SPRINGER INTERNATIONAL PUBLISHING AG Sequential Lung Nodule Synthesis Using Attribute-Guided Generative Adversarial Networks Conference 10.1007/978-3-030-87231-1_39 1 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.402 - 411 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 402 411 SZ ELECTR NETWORK 2021-09-27 MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI 000712022300039 2-s2.0-85116439173