325 citations · 506 across the 17 of their papers we have counts for
17 papers
Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
Jiacheng Xie, Hua-Chieh Shao, Can Wu +7
Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy. Representing patient a…
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality
Biling Wang, Austen Maniscalco, Ti Bai +8
Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on On…
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
Anjali Balagopal, Michael Dohopolski, Young Suk Kwon +9
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to…
Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
Anjali Balagopal, Dan Nguyen, Ti Bai +3
When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic differen…
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Biling Wang, Michael Dohopolski, Ti Bai +10
We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its effi…
Region Specific Optimization (RSO)-based Deep Interactive Registration
Ti Bai, Muhan Lin, Xiao Liang +5
Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration…
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…
Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks
Maryam Mashayekhi, Itzel Ramirez Tapia, Anjali Balagopal +6
Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose…
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…
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks
Dan Nguyen, Azar Sadeghnejad Barkousaraie, Gyanendra Bohara +4
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to becom…
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices
Roya Norouzi Kandalan, Dan Nguyen, Nima Hassan Rezaeian +5
This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adap…
Boosting radiotherapy dose calculation accuracy with deep learning
Yixun Xing, Ph. D., You Zhang +5
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Mon…
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
Anjali Balagopal, Dan Nguyen, Howard Morgan +9
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the m…
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
Ana M. Barragan-Montero, Dan Nguyen, Weiguo Lu +4
The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anato…
Fully Automated Organ Segmentation in Male Pelvic CT Images
Anjali Balagopal, Samaneh Kazemifar, Dan Nguyen +4
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pe…
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
Dan Nguyen, Xun Jia, David Sher +4
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels…