CT Image Harmonization for Enhancing Radiomics Studies
- URL: http://arxiv.org/abs/2107.01337v1
- Date: Sat, 3 Jul 2021 04:03:42 GMT
- Title: CT Image Harmonization for Enhancing Radiomics Studies
- Authors: Md Selim, Jie Zhang, Baowei Fei, Guo-Qiang Zhang, Jin Chen
- Abstract summary: RadiomicGAN is developed to mitigate the discrepancy caused by using non-standard reconstruction kernels.
A novel training approach, called Dynamic Window-based Training, has been developed to transform the pre-trained model to the medical imaging domain.
Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.
- Score: 10.643230630935781
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: While remarkable advances have been made in Computed Tomography (CT),
capturing CT images with non-standardized protocols causes low reproducibility
regarding radiomic features, forming a barrier on CT image analysis in a large
scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused
by using non-standard reconstruction kernels. RadiomicGAN consists of hybrid
neural blocks including both pre-trained and trainable layers adopted to learn
radiomic feature distributions efficiently. A novel training approach, called
Dynamic Window-based Training, has been developed to smoothly transform the
pre-trained model to the medical imaging domain. Model performance evaluated
using 1401 radiomic features show that RadiomicGAN clearly outperforms the
state-of-art image standardization models.
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