Despite remarkable progress, 3D whole brain segmentation of structural magnetic resonance imaging (MRI) into a large number of regions (>100) is still difficult due to the lack of annotated data and the limitation of GPU memory. To address these challenges, we propose a semi-supervised segmentation method based on deep neural networks to exploit the plenty of unlabeled data by extending the self-training method, and improve the U-Net model by designing a novel self-ensemble architecture and a random patch-size training strategy. Further, to reduce the model storage and computational cost, we get a compact model by knowledge distillation. Extensive experiments conducted on the MICCAI 2012 dataset demonstrate that our method dramatically outperforms previous methods and has achieved the state-of-the-art performance. Our compact model segments an MRI image within 3 s on a TITAN X GPU, which is much faster than multi-atlas based methods and previous deep learning methods.
CITATION STYLE
Zhao, Y. X., Zhang, Y. M., Song, M., & Liu, C. L. (2019). Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11766 LNCS, pp. 256–265). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32248-9_29
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