Methodological evaluation demonstrates voxel-level uncertainty quantification in brain tissue compartments, highlighting a way to reduce threshold dependency in MRI segmentation.
Segmentation is crucial in analysing volumes of brain tissue compartments in studies of healthy development and pathological changes. Probabilistic segmentation is widely used, but the choice of threshold on probability maps has large effects on the resulting tissue volumes, and there is no consensus on optimal choice of such threshold. This work presents a way to minimise this effect by quantifying the uncertainty using Shannon entropy. Entropy map contains information about uncertainties in each individual voxel and this can be used to inform about uncertainty in MRI-based tissue segmentation.
No takes yet. Share an insight, caveat, or question.
Sierhej et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: