22 citations · 36 across the 4 of their papers we have counts for
5 papers
Flash-Radiomics: A Scalable Hybrid CPU-CUDA Engine for Standardized Scalar Radiomics and Accelerated Spatial Mapping
Shanli Ding, Yiyi Hu, Ziyu Fu +5
Background and Objectives: Spatial mapping retains the spatial distribution of radiomic features, but computational cost and fragmented software limit its use. We developed Flash-R…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Xiao Liang, Jaehee Chun, Howard Morgan +4
Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) imag…
Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy
Sven Olberg, Jaehee Chun, Byong Su Choi +6
An MRI-only adaptive radiotherapy (ART) workflow is desirable for managing interfractional changes in anatomy, but producing synthetic CT (sCT) data through paired data-driven deep…
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy
Jaehee Chun, Justin C. Park, Sven Olberg +5
In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training da…