Liver Segmentation

Liver segmentation, the automated identification of liver boundaries and internal structures in medical images (primarily CT and MRI scans), aims to improve the accuracy and efficiency of diagnosis and treatment planning for liver diseases. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformers, often incorporating advanced techniques like attention mechanisms, multi-scale analysis, and adaptive loss functions to enhance segmentation accuracy and robustness across diverse image modalities and pathologies. These advancements hold significant promise for improving clinical workflows, enabling faster and more precise diagnoses, and facilitating personalized treatment strategies for liver cancer and other hepatic conditions.