Abstract
Hippocampal subfields play important and divergent roles in both memory formation and early diagnosis of many neurological diseases, but automatic subfield segmentation is less explored due to its small size and poor image contrast. In this paper, we propose an automatic learning-based hippocampal subfields segmentation framework using multi-modality 3T MR images, including T1 MRI and resting-state fMRI (rs-fMRI). To do this, we first acquire both 3T and 7T T1 MRIs for each training subject, and then the 7T T1 MRI are linearly registered onto the 3T T1 MRI. Six hippocampal subfields are manually labeled on the aligned 7T T1 MRI, which has the 7T image contrast but sits in the 3T T1 space. Next, corresponding appearance and relationship features from both 3T T1 MRI and rs-fMRI are extracted to train a structured random forest as a multi-label classifier to conduct the segmentation. Finally, the subfield segmentation is further refined iteratively by additional context features and updated relationship features. To our knowledge, this is the first work that addresses the challenging automatic hippocampal subfields segmentation using 3T routine T1 MRI and rs-fMRI. The quantitative comparison between our results and manual ground truth demonstrates the effectiveness of our method. Besides, we also find that (a) multi-modality features significantly improved subfield segmentation performance due to the complementary information among modalities; (b) automatic segmentation results using 3T multi-modality images are partially comparable to those on 7T T1 MRI.
D. Shen—This work was supported by the National Institute of Health grants 1R01 EB006733
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References
Van Leemput, K., Bakkour, A., et al.: Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557 (2009)
Yushkevich, P.A., Pluta, J.B., et al.: Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum. Brain Mapp. 36, 258–287 (2015)
Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M., Player, A., Wright, M., Roy, N., Frosch, M.P., McKee, A.C., Wald, L.L., 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, 117–137 (2015)
Pipitone, J., Park, M.T.M., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014)
Stokes, J., Kyle, C., et al.: Complementary roles of human hippocampal subfields in differentiation and integration of spatial context. J. Cogn. Neurosci. 27, 546–559 (2015)
Blessing, E.M., Beissner, F., et al.: A data-driven approach to mapping cortical and subcortical intrinsic functional connectivity along the longitudinal hippocampal axis. Hum. Brain Mapp. 37, 462–476 (2016)
Jenkinson, M., Bannister, P., et al.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002)
Huynh, T., Gao, Y., et al.: Estimating CT image from MRI data using structured random forest and auto-context model. IEEE T-MI 35, 174–183 (2016)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE T-PAMI 32, 1744–1757 (2010)
Hao, Y., Wang, T., et al.: Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation. Hum. Brain Mapp. 35, 2674–2697 (2014)
Cui, X., Liu, Y.e.a.: 3D HAAR-like features for pedestrian detection. In: ICME-2007, pp. 1263–1266. IEEE (2007)
Wang, H., Suh, J.W., et al.: Multi-atlas segmentation with joint label fusion. IEEE T-PAMI 35, 611–623 (2013)
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Wu, Z., Gao, Y., Shi, F., Jewells, V., Shen, D. (2016). Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_28
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DOI: https://doi.org/10.1007/978-3-319-47157-0_28
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