Abstract
The hippocampus is a brain structure that is involved in several cognitive functions such as memory and learning. It is a structure of great interest in the study of the healthy and diseased brain due to its relationship to several neurodegenerative pathologies. In this work, we propose a novel patch-based method that uses an ensemble of boosted neural networks to perform the hippocampus subfield segmentation on multimodal MRI. This new method minimizes both random and systematic errors using an overcomplete autocontext patch-based labeling using a novel boosting strategy. The proposed method works well on high resolution MRI but also on standard resolution images after superresolution. Finally, the proposed method was compared with a similar state-of-the-art methods showing better results in terms of both accuracy and efficiency.
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Acknowledgements
This research was supported by the Spanish UPV2016-0099 grant from Universitat Politécnica de Valencia. This study has been also carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).
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Manjón, J.V., Coupe, P. (2017). Hippocampus Subfield Segmentation Using a Patch-Based Boosted Ensemble of Autocontext Neural Networks. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_4
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DOI: https://doi.org/10.1007/978-3-319-67434-6_4
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