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 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.
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References
Milner, B.: Psychological defects produced by temporal lobe excision. Res. Publ. Assoc. Res. Nerv. Ment. Dis. 36, 244–257 (1958)
Petersen, R., et al.: Memory and MRI-based hippocampal volumes in aging and AD. Neurology 54(3), 581–587 (2000)
Cendes, F., et al.: MRI volumetric measurement of amygdala and hippocampus in temporal lobe epilepsy. Neurology 43(4), 719–725 (1993)
Altshuler, L.L., et al.: Amygdala enlargement in bipolar disorder and hippocampal reduction in schizophrenia: an MRI study demonstrating neuroanatomic specificity. Arch. Gen. Psychiatry 55(7), 663 (1998)
Braak, H., Braak, E.: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991)
Chupin, M., et al.: Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6), 579–587 (2009)
Jack, C., et al.: Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52(7), 1397–1403 (1999)
Chakravarty, M., et al.: Performing label-fusion-based segmentation using multiple automatically generated templates. Hum. Brain Mapp. 10(34), 2635–2654 (2013)
Yushkevich, P.A., et al.: Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum. Brain Mapp. 36(1), 258–287 (2015)
Van Leemput, K., et al.: Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19(6), 549–557 (2009)
Iglesias, J.E., et al.: A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage 115(15), 117–137 (2015)
Winterburn, J.L., et al.: A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage 74, 254–265 (2013)
Giraud, R., et al.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)
Pipitone, J.L., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101(1), 494–512 (2014)
Avants, B.B., et al.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)
Barnes, C., et al.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3) (2009)
Coupé, P., et al.: Adaptive multiresolution non-local means filter for 3D MR image denoising. IET Image Process. 6(5), 558–568 (2012)
Zijdenbos, A.P., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)
Coupé, P., et al.: Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage 83, 245–261 (2013)
Manjón, J.V., et al.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Acknowledgements
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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Romero, J.E., Coupe, P., Manjón, J.V. (2016). High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2016. Lecture Notes in Computer Science(), vol 9993. Springer, Cham. https://doi.org/10.1007/978-3-319-47118-1_15
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DOI: https://doi.org/10.1007/978-3-319-47118-1_15
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