Multi-atlas subcortical segmentation: an orchestration of 3D fully convolutional network and generalized mixture function-Reference-Cited by-同舟云学术

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Multi-atlas subcortical segmentation: an orchestration of 3D fully convolutional network and generalized mixture function

Published:2023-07 Issue:4 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Wu Jiong,He Shuan,Zhou Shuang

Funder

the Youth fund of Hunan Province Department of Education

the Scientific Research Project of Hunan University of Arts and Science

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-023-01415-0.pdf

Reference35 articles.

1. Apostolova, L.G., Dinov, I.D., Dutton, R.A., Hayashi, K.M., Toga, A.W., Cummings, J.L., Thompson, P.M.: 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. Brain 129(11), 2867–2873 (2006). https://doi.org/10.1093/brain/awl274

2. Cobzas, D., Sun, H., Walsh, A.J., Lebel, R.M., Blevins, G., Wilman, A.H.: Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. J. Magn. Reson. Imaging 42(6), 1601–1610 (2015). https://doi.org/10.1002/jmri.24951

3. Cerliani, L., Mennes, M., Thomas, R.M., Di Martino, A., Thioux, M., Keysers, C.: Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder. JAMA Psychiat. 72(8), 767–777 (2015). https://doi.org/10.1001/jamapsychiatry.2015.0101

4. Geevarghese, R., Lumsden, D.E., Hulse, N., Samuel, M., Ashkan, K.: Subcortical structure volumes and correlation to clinical variables in Parkinson’s disease. J. Neuroimaging 25(2), 275–280 (2015). https://doi.org/10.1111/jon.12095

5. Tang, X., Qin, Y., Wu, J., Zhang, M., Zhu, W., Miller, M.I.: Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer’s disease. Magn. Reson. Imaging 34(8), 1087–1099 (2016). https://doi.org/10.1016/j.mri.2016.05.001

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