Quantitative Segmentation

Quantitative segmentation aims to precisely delineate objects or regions of interest within images, particularly in medical imaging where accurate measurements are crucial for diagnosis and treatment. Current research emphasizes improving segmentation accuracy and robustness, focusing on techniques like incorporating uncertainty quantification, human-in-the-loop refinement, and advanced architectures such as UNets and Swin UNETR models, often coupled with dimensionality reduction methods like UMAP. These advancements are vital for enhancing the reliability and clinical utility of automated image analysis, addressing challenges like inter-rater variability and the detection of out-of-distribution data.