136 citations · 155 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
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…
Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning
Samaneh Kazemifar, Anjali Balagopal, Dan Nguyen +4
Inter-and intra-observer variation in delineating regions of interest (ROIs) occurs because of differences in expertise level and preferences of the radiation oncologists. We evalu…