Reliable Segmentation

Reliable image segmentation aims to accurately delineate objects or regions of interest within images, particularly in challenging domains like medical imaging where inconsistencies in manual annotations are common. Current research focuses on improving segmentation robustness through techniques like synthetic data generation using diffusion models and generative adversarial networks to augment training datasets, and incorporating uncertainty quantification into model training and evaluation, often employing U-Net architectures and ensemble methods. These advancements are crucial for enhancing the reliability and clinical applicability of automated segmentation in diverse fields, from medical diagnosis to materials science, by reducing annotation bias and improving the accuracy of automated analyses.