Segmentation Uncertainty

Segmentation uncertainty, the quantification of confidence in image segmentation predictions, aims to improve the reliability and robustness of automated image analysis, particularly in critical applications like medical imaging. Current research focuses on developing methods to estimate both aleatoric (data-inherent) and epistemic (model-related) uncertainty, employing techniques such as Bayesian neural networks, deep ensembles, and probabilistic models like diffusion probabilistic models, often integrated with attention mechanisms or level set methods. Accurate uncertainty quantification is crucial for building trust in automated systems and enabling more informed decision-making in various fields, from medical diagnosis to robotics.