Organ Segmentation

Organ segmentation, the automated identification and delineation of organs in medical images, aims to improve efficiency and accuracy in diagnosis and treatment planning. Current research focuses on developing robust and efficient deep learning models, often based on U-Net architectures or transformers, to address challenges like class imbalance, limited annotated data, and the segmentation of small or irregularly shaped organs. These advancements are significantly impacting healthcare by automating time-consuming tasks, improving the precision of radiotherapy, and facilitating more accurate disease diagnosis and personalized treatment strategies. Furthermore, research is exploring methods to improve model generalizability across different datasets and imaging modalities, and to quantify the uncertainty associated with model predictions.