15 citations · 58 across the 9 of their papers we have counts for
5 papers · 1 filter
Registration-Guided Deep Learning Image Segmentation for Cone Beam CT-based Online Adaptive Radiotherapy
Lin Ma, Weicheng Chi, Howard E. Morgan +8
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficientl…
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
Ti Bai, Anjali Balagopal, Michael Dohopolski +7
Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revi…
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
Anjali Balagopal, Howard Morgan, Michael Dohopoloski +9
Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem,…
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study
Anjali Balagopal, Dan Nguyen, Maryam Mashayekhi +6
Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, th…
Deep learning based CT-to-CBCT deformable image registration for autosegmentation in head and neck adaptive radiation therapy
Xiao Liang, Howard Morgan, Dan Nguyen +1
The purpose of this study is to develop a deep learning based method that can automatically generate segmentations on cone-beam CT (CBCT) for head and neck online adaptive radiatio…