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Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation
To dispense with the manual annotation requirement, we propose to train a model to adaptively transfer the annotation from the cerebellum on the Allen Brain Human Brain Atlas to the BigBrain in an unsupervised manner, taking into account the different staining and spacing between sections.
Local semi-supervised approach to brain tissue classification in child brain MRI
Most segmentation methods in child brain MRI are supervised and are based on global intensity distributions of major brain structures.
Novel solutions toward high accuracy automatic brain tissue classification in young children
A poor and highly variable grey matter and white matter contrast on T1-weighted MR scans of developing brains complicates the automatic categorization of voxels into major tissue classes using state-of-the-art classification methods (Partial Volume Estimation).
Image and Video Processing Statistical Mechanics Quantitative Methods I.4.6
