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Statistical inference of the inter-sample Dice distribution for discriminative CNN brain lesion segmentation models
For a trained discriminative CNN to be used in clinical practice, the patient's radiological features are inputted into the model, in which case a conditional distribution of segmentations is produced.
Modelling brain lesion volume in patches with CNN-based Poisson Regression
The ISLES2015 (SISS) data is used to train and evaluate the model, which by estimating lesion volume from raw features, accurately identified the lesion image with the larger lesion volume for 86% of paired sample patches.
Exploiting bilateral symmetry in brain lesion segmentation
Specifically, we use nonlinear registration of a neuroimage to a reflected version of itself ("reflective registration") to determine for each voxel its homologous (corresponding) voxel in the other hemisphere.
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