Chest CBCT-based synthetic CT using cycle-consistent adversarial network with histogram matching

2021 ◽  
Author(s):  
Richard Qiu ◽  
Yang Lei ◽  
Aparna Kesarwala ◽  
Kristin Higgins ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Adversarial Network ◽  
Histogram Matching ◽  
Synthetic Ct

scholarly journals OC-0294: MR-based synthetic CT with conditional Generative Adversarial Network for prostate RT planning

2018 ◽  
Vol 127 ◽  
pp. S151-S152 ◽  
Author(s):  
M.H.F. Savenije ◽  
M. Maspero ◽  
A.M. Dinkla ◽  
P.R. Seevinck ◽  
C.A.T. Van den Berg
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct

Evaluation of conventional and deep learning based image harmonization methods in radiomics studies

2021 ◽  
Author(s):  
Florent Tixier ◽  
Vincent Jaouen ◽  
Clément Hognon ◽  
Olivier Gallinato ◽  
Thierry Colin ◽  
...  
Keyword(s):  
Prediction Models ◽  
Texture Features ◽  
Reference Image ◽  
P Value ◽  
Survival Prediction ◽  
Shape Features ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Histogram Matching ◽  
The Impact

Abstract Objective: To evaluate the impact of image harmonization on outcome prediction models using radiomics. Approach: 234 patients from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset with T1 MRI were enrolled in this study. Images were harmonized through a reference image using histogram matching (HHM) and a generative adversarial network (GAN)-based method (HGAN). 88 radiomics features were extracted on HHM, HGAN and original (HNONE) images. Wilcoxon paired test was used to identify features significantly impacted by the harmonization protocol used. Radiomic prediction models were built using feature selection with the Least Absolute Shrinkage and Selection Operator (LASSO) and Kaplan-Meier analysis. Main results: More than 50% of the features (49/88) were statistically modified by the harmonization with HHM and 55 with HGAN (adjusted p-value < 0.05). The contribution of histogram and texture features selected by the LASSO, in comparison to shape features that were not impacted by harmonization, was higher in harmonized datasets (47% for Hnone, 62% for HHM and 71% for HGAN). Both image-based harmonization methods allowed to split patients into two groups with significantly different survival (p<0.05). With the HGAN images, we were also able to build and validate a model using only features impacted by the harmonization (median survivals of 189 vs. 437 days, p=0.006) Significance: Data harmonization in a multi-institutional cohort allows to recover the predictive value of some radiomics features that was lost due to differences in the image properties across centers. In terms of ability to build survival prediction models in the BRATS dataset, the loss of power from impacted histogram and heterogeneity features was compensated by the selection of additional shape features. The harmonization using a GAN-based approach outperformed the histogram matching technique, supporting the interest for the development of new advanced harmonization techniques for radiomic analysis purposes.

scholarly journals Dixon-based thorax synthetic CT generation using Generative Adversarial Network

2020 ◽  
Vol 3-4 ◽  
pp. 100010
Author(s):  
Atallah Baydoun ◽  
Ke Xu ◽  
Huan Yang ◽  
Feifei Zhou ◽  
Jin Uk Heo ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct

Multi‐sequence MR image‐based synthetic CT generation using a generative adversarial network for head and neck MRI‐only radiotherapy

2020 ◽  
Vol 47 (4) ◽  
pp. 1880-1894 ◽  
Author(s):  
Mengke Qi ◽  
Yongbao Li ◽  
Aiqian Wu ◽  
Qiyuan Jia ◽  
Bin Li ◽  
...  
Keyword(s):  
Head And Neck ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct

scholarly journals Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy

2018 ◽  
Vol 63 (18) ◽  
pp. 185001 ◽  
Author(s):  
Matteo Maspero ◽  
Mark H F Savenije ◽  
Anna M Dinkla ◽  
Peter R Seevinck ◽  
Martijn P W Intven ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Dose Evaluation ◽  
Adversarial Network ◽  
Synthetic Ct
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