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1Department of Neuroscience, Central Clinical School,Monash University, 2Department of Biomedical Engineering,The Johns Hopkins University, 3Department of Psychiatry,National Institute of Mental Health & Neuro Sciences (NIMHANS), 4Faculty of Computer Science,Dalhousie University, 5Department of Electrical and Computer Engineering,The Johns Hopkins University, 6Department of Computer Science,The Johns Hopkins University, 7Brain and Mind Institute, Department for Statistical and Actuarial Sciences, Department for Computer Science,Western University, 8Monash Biomedical Imaging,Monash University
Chapters
- 00:05Introduction
- 00:41Module 1: Automatic Cerebellum Anatomical Parcellation Using U-Net Locally Constrained Optimization (ACAPULCO)
- 04:44Module 2: SUIT Cerebellum-Optimized Voxel-Based Morphometry
- 08:47Results: Examining Cerebellum Grey Matter Morphometry in Friedreich Ataxia Patients and Healthy Controls
- 11:04Conclusion
Beyincik gri madde morfometrisini incelemek için standartlaştırılmış bir boru hattı sunulmuştur. Boru hattı, optimize edilmiş ve otomatik beyincik parselasyonu için yüksek çözünürlüklü, son teknoloji ürünü yaklaşımları ve hacimsel niceleme için beyinciğin voksel tabanlı kaydını birleştirir.
