Cancer Segmentation

Cancer segmentation, the automated identification and delineation of cancerous regions in medical images, aims to improve diagnostic accuracy and treatment planning. Current research focuses on developing robust deep learning models, including U-Net variations and transformer-based architectures, often incorporating attention mechanisms and leveraging anatomical information to improve segmentation accuracy, particularly for challenging cases like rectal cancer. These advancements address limitations posed by data scarcity and inter-observer variability, ultimately impacting clinical workflows by assisting radiologists and potentially leading to more personalized cancer care.