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Found 5 papers, 3 papers with code
SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
To address this, we present SinoSynth, a physics-based degradation model that simulates various CBCT-specific artifacts to generate a diverse set of synthetic CBCT images from high-quality CT images without requiring pre-aligned data.
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images
To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature.
A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing
A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation.
Medical Image Synthesis with Deep Convolutional Adversarial Networks
To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.
