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arXiv · 2107.01337

CT Image Harmonization for Enhancing Radiomics Studies

Md Selim·Jie Zhang·Baowei Fei·Guo-Qiang Zhang·Jin Chen
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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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Md Selim, Jie Zhang, Baowei Fei, Guo-Qiang Zhang, Jin Chen. 2021-07-03. CT Image Harmonization for Enhancing Radiomics Studies. https://arxiv.org/abs/2107.01337

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