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3 November 2020 Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images
Julián Alberto Palladino, Diego Fernandez Slezak, Enzo Ferrante
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Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 1158302 (2020) https://doi.org/10.1117/12.2579548
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru
Abstract
Automatic segmentation of white matter hyperintensities in magnetic resonance images is of paramount clinical and research importance. Quantification of these lesions serve as a predictor for risk of stroke, dementia and mortality. During the last years, convolutional neural networks (CNN) specifically tailored for biomedical image segmentation have outperformed all previous techniques in this task. However, they are extremely data dependent, and maintain a good performance only when data distribution between training and test datasets remains unchanged. When such distribution changes but we still aim at performing the same task, we incur in a domain adaptation problem (e.g. using a different MR machine or different acquisition parameters for training and test data). In this work, we explore the use of cycle-consistent adversarial networks (CycleGAN) to perform unsupervised domain adaptation on multicenter MR images with brain lesions. We aim at learning a mapping function to transform volumetric MR images between domains, which are characterized by different medical centers and MR machines with varying brand, model and configuration parameters. Our experiments show that CycleGAN allows us to reduce the Jensen-Shannon divergence between MR domains, enabling automatic segmentation with CNN models on domains where no labeled data was available.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Julián Alberto Palladino, Diego Fernandez Slezak, and Enzo Ferrante "Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 1158302 (3 November 2020); https://doi.org/10.1117/12.2579548
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