STAN-CT: Standardizing CT Image using Generative Adversarial Network
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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