1. USING A GENERATIVE ADVERSARIAL NETWORK FOR CT NORMALIZATION AND ITS IMPACT ON RADIOMIC FEATURES
- Author
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Wei, Leihao, Lin, Yannan, Hsu, William, and IEEE
- Subjects
lung cancer ,radiomics ,generative adversarial networks ,deep neural networks ,denoising ,eess.IV ,cs.CV ,cs.LG ,stat.ML - Abstract
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifyingand classifying potentially malignant pulmonary nodules on chest CT scans usingmorphology and texture-based (radiomic) features. However, radiomic featuresare sensitive to differences in acquisitions due to variations in dose levelsand slice thickness. This study investigates the feasibility of generating anormalized scan from heterogeneous CT scans as input. We obtained projectiondata from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and50% dose and reconstructing the scans at 1.0mm and 2.0mm slice thickness. A 3Dgenerative adversarial network (GAN) was used to simultaneously normalizereduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice(1.0mm) images. We evaluated the normalized image quality using peaksignal-to-noise ratio (PSNR), structural similarity index (SSIM) and LearnedPerceptual Image Patch Similarity (LPIPS). Our GAN improved perceptualsimilarity by 35%, compared to a baseline CNN method. Our analysis also showsthat the GAN-based approach led to a significantly smaller error (p-value <0.05) in nine studied radiomic features. These results indicated that GANscould be used to normalize heterogeneous CT images and reduce the variabilityin radiomic feature values.
- Published
- 2020