High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion
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 due to its relationship to neurodegenerative processes such as the Alzheimer’s disease. In this work, we propose a novel multispectral multiatlas patch-based method to automatically segment hippocampus subfields using high resolution T1-weighted and T2-weighted magnetic resonance images (MRI). The proposed method works well also on standard resolution images after superresolution and consistently performs better than monospectral version. Finally, the proposed method was compared with similar state-of-the-art methods showing better results in terms of both accuracy and efficiency.
KeywordsPatch Size High Resolution Image Cornu Ammonis Hippocampus Subfield Dimension Voxels
This research was supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been 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), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.
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