451 citations · 791 across the 8 of their papers we have counts for
8 papers
Sub-second photon dose prediction via transformer neural networks
Oscar Pastor-Serrano, Peng Dong, Charles Huang +2
Fast dose calculation is critical for online and real time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computatio…
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans
Charles Huang, Varun Vasudevan, Oscar Pastor-Serrano +7
Objective: Knowledge based planning (KBP) typically involves training an end-to-end deep learning model to predict dose distributions. However, training end-to-end methods may be a…
Meta-optimization for Fully Automated Radiation Therapy Treatment Planning
Charles Huang, Yusuke Nomura, Yong Yang +1
Objective: Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propo…
Fully Automated Noncoplanar Radiation Therapy Treatment Planning
Charles Huang, Yong Yang, Lei Xing
Noncoplanar radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment plann…
Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface
Charles Huang, Yong Yang, Neil Panjwani +2
Objective: Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes…
Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations
Charles Huang, Masoud Badiei, Hyunseok Seo +5
Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in current radiation oncology pipelines and is often time consuming and labor intensive. In this paper, we propose an…
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan +5
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this re…
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
Hyunseok Seo, Charles Huang, Maxime Bassenne +2
Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it…