OAR Segmentation

Organ-at-risk (OAR) segmentation, the automated identification of anatomical structures in medical images, is crucial for optimizing radiotherapy planning and other medical applications. Current research focuses on improving the accuracy and efficiency of 3D OAR segmentation using deep learning architectures, such as multi-scale fusion networks and combinations of convolutional and graph neural networks, often incorporating techniques to handle noisy or multi-rater annotations. These advancements aim to reduce the time clinicians spend manually correcting automated segmentations, ultimately improving the speed and accuracy of treatment planning and diagnosis. Furthermore, research is extending these methods to 4D representations to capture dynamic changes over time, enabling more comprehensive analysis of complex medical phenomena.