Liver Tumor Segmentation

Liver tumor segmentation, the automated identification of liver tumors in medical images, aims to improve diagnostic accuracy and efficiency in cancer care. Current research heavily utilizes deep learning, focusing on advanced architectures like U-Net variations, transformers, and hybrid CNN-transformer models, often incorporating attention mechanisms and multi-scale feature fusion to address challenges posed by tumor heterogeneity and image artifacts. These advancements are improving segmentation accuracy and robustness, particularly for small tumors, and facilitating applications such as surgical planning and treatment monitoring. The development of semi-supervised and self-supervised learning techniques, along with the use of synthetic data for training and validation, is also a significant area of focus to overcome limitations in annotated data availability.