STAN-CT: Standardizing CT Image using Generative Adversarial Network
This addresses a challenge in large-scale cross-center CT radiomic studies for lung malignancy diagnostics, enabling more consistent image analysis, but it is incremental as it builds on existing GAN-based standardization techniques.
The paper tackles the problem of discrepancies in CT images due to varying imaging protocols or scanners, presenting STAN-CT, an end-to-end solution that uses a novel GAN model and an automatic DICOM pipeline to standardize images, with results showing significant improvements in training efficiency and performance over state-of-the-art methods.
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.