Target Volume

Target volume delineation, crucial for radiotherapy planning and other applications like image forgery detection, aims to precisely define regions of interest within medical images or other data. Current research focuses on automating this process using deep learning models, particularly U-Net and transformer-based architectures, often incorporating anatomical priors or large language models to improve accuracy and reduce bias. These advancements promise to improve the efficiency and consistency of radiotherapy treatment planning, potentially leading to better patient outcomes and reducing the workload on medical professionals. Furthermore, similar techniques are being explored for other applications requiring precise object identification and segmentation.