Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-01-008 ISMRM Abstract

Comparative Evaluation of Automated Cerebellar Parcellation Pipelines in Degenerative Cerebellar Diseases

Primary: Analysis Methods - Data Processing
Secondary: Neuro
664-01-008 · Cerebellar Function · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row E
Keywords: Cerebellum Ataxia Structural MRI
Accepted
Sebastian A Collins1, Lachlan T Strike1,2, Ian H Harding 1,3, TRACK-FA Neuroimaging Consortium
1Brain and Mental Health, QIMR Berghofer, Australia
2School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
3School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
Presenting Author: Ian H Harding

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References

1. Lindig T, Bender B, Kumar VJ, et al. Pattern of Cerebellar Atrophy in Friedreich's Ataxia-Using the SUIT Template. Cerebellum 2019;18(3):435-47. doi:10.1007/s12311-019-1008-z [doi]
2. Carass A, Cuzzocreo JL, Han S, et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage 2018;183:150-72. doi:10.1016/j.neuroimage.2018.08.003 [doi]
3. Han S, Carass A, He Y, Prince JL. Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization. Neuroimage 2020;218:116819. doi:10.1016/j.neuroimage.2020.116819 [doi]
4. Faber J, Kugler D, Bahrami E, Heinz LS, Timmann D, Ernst TM, et al. CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. Neuroimage 2022;264:119703. doi:10.1016/j.neuroimage.2022.119703 [doi]
5. Morell-Ortega S, Ruiz-Perez M, Gadea M, Vivo-Hernando R, Rubio G, Aparici F, et al. DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI. Neuroimage 2025;308:121063. doi:10.1016/j.neuroimage.2025.121063 [doi]
6. Diedrichsen J. A spatially unbiased atlas template of the human cerebellum. Neuroimage 2006;33(1):127-38. doi:10.1016/j.neuroimage.2006.05.056 [doi]
7. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54(3):2033-44. doi:10.1016/j.neuroimage.2010.09.025 [doi]
8. Gaser C, Dahnke R, Thompson PM, Kurth F, Luders E, The Alzheimer's Disease Neuroimaging I. CAT: a computational anatomy toolbox for the analysis of structural MRI data. Gigascience 2024;13:giae049. doi:10.1093/gigascience/giae049 [doi]
9. Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26(3):839-51. doi:10.1016/j.neuroimage.2005.02.018 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/664-01-008