Shaobin Wang

Accurate delineation of the esophageal clinical target volume (CTV) is challenging due to extended longitudinal coverage and substantial slice-wise shape variation. Existing deep learning methods typically require extensive manual revision. Interactive segmen...

This study proposes an automated quality assurance (QA) method for radiation therapy structure delineation based on the RT contour QA software, addressing issues such as low efficiency in delineating clinical radiation therapy regions of interest (ROIs), sign...

This study proposed the residual dynamic transformer-nnU-Net (RDT-nnU-Net) for automated delineation of esophageal cancer and adjacent organs at risk (OARs) in radiotherapy, aiming to improve accuracy, coverage, and adaptability across radiotherapy strategies.