Tonghe Wang

TL;DR: A comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets is provided and the statistics of all the cited works from various aspects are analyzed, revealing the popularity and future trend ofDL-based medical image registration.

TL;DR: A novel deep learning-based approach with a GAN strategy to segment multiple OARs in the thorax using chest CT images is investigated and demonstrated its feasibility and reliability, and is a potentially valuable method for improving the efficiency of chest radiotherapy treatment planning.

TL;DR: A novel learning-based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs and offers strong potential for supporting near real-time MRI-only treatment planning in the brain and pelvis.

TL;DR: A novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate is developed, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation.

TL;DR: The authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images that shows superior image quality as compared to the scatter correction method, reducing noise and artefact severity.