Anatomical Prior

Anatomical priors leverage existing knowledge of organ and tissue structure to improve the accuracy and efficiency of medical image analysis. Current research focuses on integrating this prior information into deep learning models, particularly convolutional neural networks and transformers, for tasks like organ segmentation and landmark detection across various imaging modalities (e.g., CT, MRI, OCT). This approach addresses challenges posed by limited training data, image variability, and complex anatomical structures, leading to improved diagnostic accuracy and potentially reducing the need for time-consuming manual annotation. The resulting advancements have significant implications for clinical workflows and the development of more robust and reliable computer-aided diagnosis tools.