Style Transfer Using Generative Adversarial Networks for Multi-site MRI Harmonization
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Alyssa H. Zhu | Neda Jahanshad | Yaqiong Chai | Sophia I. Thomopoulos | Mengting Liu | Piyush Maiti | Hosung Kim
[1] Russell T. Shinohara,et al. Harmonization of cortical thickness measurements across scanners and sites , 2017, NeuroImage.
[2] Harini Veeraraghavan,et al. Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation , 2020, MICCAI.
[3] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[4] Alan C. Evans,et al. Fast and robust parameter estimation for statistical partial volume models in brain MRI , 2004, NeuroImage.
[5] Aaron Carass,et al. DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. , 2019, Magnetic resonance imaging.
[6] Yu Qiao,et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.
[7] Paul M. Thompson,et al. Scanner invariant representations for diffusion MRI harmonization , 2019, Magnetic resonance in medicine.
[8] Jan Kautz,et al. Multimodal Unsupervised Image-to-Image Translation , 2018, ECCV.
[9] Peipeng Liang,et al. Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings , 2020, Scientific Data.
[10] Li Wang,et al. Harmonization of Infant Cortical Thickness Using Surface-to-Surface Cycle-Consistent Adversarial Networks , 2019, MICCAI.
[11] Peter A. Calabresi,et al. A Disentangled Latent Space for Cross-Site MRI Harmonization , 2020, MICCAI.