185 citations · 307 across the 5 of their papers we have counts for
5 papers
RMSim: Controlled Respiratory Motion Simulation on Static Patient Scans
Donghoon Lee, Ellen Yorke, Masoud Zarepisheh +2
This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep lear…
Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy
Donghoon Lee, Sadegh R Alam, Jue Jiang +3
Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor…
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
Jue Jiang, Yu Chi Hu, Neelam Tyagi +5
We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ…
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets
Jue Jiang, Yu-Chi Hu, Neelam Tyagi +4
Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments trai…
A patient-specific respiratory model of anatomical motion for radiation treatment planning
Qinghui Zhang, Alex Pevsner, Agung Hertanto +4
Modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have dev…