دورية أكاديمية
Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks
العنوان: | Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks |
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المؤلفون: | Kossen, T., Subramaniam, P., Madai, V., Hennemuth, A., Hildebrand, K., Hilbert, A., Sobesky, J., Livne, M., Galinovic, I., Khalil, A., Fiebach, J., Frey, D. |
المصدر: | Computers in Biology and Medicine |
سنة النشر: | 2021 |
المجموعة: | Max Planck Society: MPG.PuRe |
الوصف: | Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging. Here, the brain's unique structure allows for re-identification and thus requires non-conventional anonymization. Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties. Analyzing brain vessel segmentation, we trained 3 GANs on time-of-flight (TOF) magnetic resonance angiography (MRA) patches for image-label generation: 1) Deep convolutional GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with spectral normalization (WGAN-GP-SN). The generated image-labels from each GAN were used to train a U-net for segmentation and tested on real data. Moreover, we applied our synthetic patches using transfer learning on a second dataset. For an increasing number of up to 15 patients we evaluated the model performance on real data with and without pre-training. The performance for all models was assessed by the Dice Similarity Coefficient (DSC) and the 95th percentile of the Hausdorff Distance (95HD). Comparing the 3 GANs, the U-net trained on synthetic data generated by the WGAN-GP-SN showed the highest performance to predict vessels (DSC/95HD 0.85/30.00) benchmarked by the U-net trained on real data (0.89/26.57). The transfer learning approach showed superior performance for the same GAN compared to no pre-training, especially for one patient only (0.91/24.66 vs. 0.84/27.36). In this work, synthetic image-label pairs retained generalizable information and showed good performance for vessel segmentation. Besides, we showed that synthetic patches can be used in a transfer learning approach with independent data. This paves the way to overcome the challenges of scarce data and anonymization in medical imaging. |
نوع الوثيقة: | article in journal/newspaper |
اللغة: | English |
العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/33618105; http://hdl.handle.net/21.11116/0000-0008-22B5-5Test |
الإتاحة: | https://doi.org/10.1016/j.compbiomed.2021.104254Test http://hdl.handle.net/21.11116/0000-0008-22B5-5Test |
رقم الانضمام: | edsbas.2CCD6230 |
قاعدة البيانات: | BASE |
الوصف غير متاح. |