Atlas Based Segmentation

Atlas-based segmentation leverages a pre-segmented anatomical "atlas" image to automatically segment corresponding structures in new images, improving efficiency and consistency compared to manual annotation. Current research focuses on developing robust and accurate registration methods, often employing deep learning architectures like convolutional neural networks and level set methods, to map the atlas onto target images, even with limited training data or significant anatomical variations. This technique finds broad application across diverse medical imaging modalities, enabling faster and more objective analysis for applications ranging from diagnosing diseases like prostate cancer to guiding neurosurgical procedures.