Voxel Scale Uncertainty

Voxel-scale uncertainty quantification in medical image analysis aims to assess the reliability of automated segmentation results at the individual voxel level, improving the trustworthiness and interpretability of deep learning models. Current research focuses on developing novel methods, including ensemble learning and Bayesian approaches, to estimate uncertainty and improve its correspondence with actual errors, often incorporating structural information beyond individual voxels (e.g., lesion- or structure-level uncertainty). This work is crucial for enhancing the clinical utility of automated segmentation tools by identifying potentially erroneous predictions and improving the accuracy and reliability of diagnoses and treatment planning.