Organ Annotation

Organ annotation in medical imaging focuses on automatically identifying and segmenting different organs within medical scans, primarily to improve diagnostic accuracy and treatment planning. Current research emphasizes developing robust and efficient deep learning models, often employing semi-supervised learning techniques to leverage both labeled and unlabeled data, and exploring model adaptation and ensemble methods to address data scarcity. This work is crucial for reducing the time and cost associated with manual annotation, ultimately leading to faster and more accurate diagnoses and improved patient care. A key challenge remains ensuring robustness across diverse clinical scenarios and handling complex or poorly defined organ boundaries.