Tumor Segmentation

Tumor segmentation, the automated identification and delineation of tumors in medical images, aims to improve diagnostic accuracy and treatment planning. Current research emphasizes robust segmentation across diverse imaging modalities (MRI, CT, PET) and scanners, often employing deep learning architectures like U-Net, Swin-UNet, and transformers, and addressing challenges such as missing modalities and domain shifts through techniques like knowledge distillation, multi-task learning, and data augmentation. These advancements hold significant promise for improving cancer diagnosis, treatment personalization, and ultimately, patient outcomes.