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arXiv · 2004.01307

STAN-CT: Standardizing CT Image using Generative Adversarial Network

Md Selim·Jie Zhang·Baowei Fei·Guo-Qiang Zhang·Jin Chen
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Abstract

Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery. However, the use of personalized imaging protocols poses a challenge in large-scale cross-center CT image radiomic studies. We present an end-to-end solution called STAN-CT for CT image standardization and normalization, which effectively reduces discrepancies in image features caused by using different imaging protocols or using different CT scanners with the same imaging protocol. STAN-CT consists of two components: 1) a novel Generative Adversarial Networks (GAN) model that is capable of effectively learning the data distribution of a standard imaging protocol with only a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control that ensure the generation of high-quality standard DICOM images. Experimental results indicate that the training efficiency and model performance of STAN-CT have been significantly improved compared to the state-of-the-art CT image standardization and normalization algorithms.

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Md Selim, Jie Zhang, Baowei Fei, Guo-Qiang Zhang, Jin Chen. 2020-04-02. STAN-CT: Standardizing CT Image using Generative Adversarial Network. https://arxiv.org/abs/2004.01307

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