Abstract
Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. In this work, we propose a DL-based label fusion strategy (VoteNet) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach. We also provide an experimental analysis of the upper performance bound achievable by our method. While unlikely achievable in practice, this bound suggests room for further performance improvements. Lastly, to address the runtime disadvantage of standard MAS, all our results make use of a fast DL registration approach.
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Notes
- 1.
LPBA40 contains 40 3D brain MRIs with 56 manually segmented structures. Preprocessing includes affine registration to the MNI152 atlas and histogram equalization.
- 2.
MABMIS uses Diffeomorphic Demons for registration and hence results are not directly comparable to ours; we use Quicksilver for all other label fusion methods.
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Acknowledgements
Research reported in this work was supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF) under award numbers NSF EECS-1711776 and NIH 1R01AR072013. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NSF.
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Ding, Z., Han, X., Niethammer, M. (2019). VoteNet: A Deep Learning Label Fusion Method for Multi-atlas Segmentation. 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_23
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