Multi Organ

Multi-organ research focuses on developing methods for simultaneously analyzing and treating multiple interconnected organ systems, addressing the limitations of single-organ approaches. Current research heavily utilizes deep learning, particularly transformer and U-Net architectures, with advancements in self-supervised and semi-supervised learning to overcome data scarcity challenges in medical image segmentation and analysis. These efforts aim to improve diagnostic accuracy, treatment planning, and ultimately patient outcomes across various medical applications, including radiotherapy and surgical planning. The development of large, multi-organ datasets and novel evaluation metrics are also key areas of focus.