Abstract
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.
S. Suh and S. Cheon—These authors contributed equally to this work.
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Acknowledgment
This work is supported by Korea Institute of Science and Technology Europe project 12120. KIST Europe collaborated with KIST Seoul (Hanyang University-KIST biomedical fellowship program) and Catholic Medical Center in Korea. The authors gave special thanks to Soyun Chang and Kyongmin Beck for activate participation to the visual turing test and valuable feedback.
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Suh, S., Cheon, S., Chang, DJ., Lee, D., Lee, Y.O. (2021). Sequential Lung Nodule Synthesis Using Attribute-Guided Generative Adversarial Networks. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_39
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