Abstract
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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (NSFC) Grants 61773376, 61721004, 61836014, 31870984 and Beijing Science and Technology Program Grant Z181100008918010.
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Zhao, YX., Zhang, YM., Song, M., Liu, CL. (2019). Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_29
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DOI: https://doi.org/10.1007/978-3-030-32248-9_29
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