Mr. Yijie Yuan Profile

Yijie Yuan

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Proceedings Article | 3 April 2024 Presentation + Paper

End-to-end deep learning restoration of GLCM features from blurred and noisy images

Yijie Yuan, J. Webster Stayman, Grace Gang

Proceedings Volume 12927, 129271C (2024) https://doi.org/10.1117/12.3006205

KEYWORDS: Cooccurrence matrices, Radiomics, Deep learning, Image restoration, Lung, Testing and analysis, Computed tomography, Network architectures

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Radiomics involves the quantitative analysis of medical images to provide useful information for a range of clinical applications including disease diagnosis, treatment assessment, etc. However, the generalizability of radiomics model is often challenged by undesirable variability in radiomics feature values introduced by different scanners and imaging conditions. To address this issue, we developed a novel dual-domain deep learning algorithm to recover ground truth feature values given known blur and noise in the image. The network consists of two U-Nets connected by a differentiable GLCM estimator. The first U-Net restores the image, and the second restores the GLCM. We evaluated the performance of the network on lung CT image patches in terms of both closeness of recovered feature values to the ground truth and accuracy of classification between normal and COVID lungs. Performance was compared with an image restoration-only method and an analytical method developed in previous work. The proposed network outperforms both methods, achieving GLCM with the lowest mean-absolute-error from ground truth. Recovered GLCM feature values from the proposed method, on average, is within 2.19% error to the ground truth. Classification performance using recovered features from the network closely matches the “best case” performance achieved using ground truth feature values. The deep learning method has been shown to be a promising tool for radiomics standardization, paving the way for more reliable and repeatable radiomics models.

Proceedings Article | 7 April 2023 Poster + Paper

Deep learning CT image restoration using system blur models

Yijie Yuan, Matthew Tivnan, Grace Gang, J. Webster Stayman

Proceedings Volume 12463, 124634J (2023) https://doi.org/10.1117/12.2655806

KEYWORDS: Image restoration, Deep learning, Computed tomography, Education and training, Network architectures, X-ray computed tomography, Data modeling, Performance modeling

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Restoration of images contaminated by blur is an important processing tool across modalities including computed tomography where the blur induced by various system factors can be complex with dependencies on acquisition and reconstruction protocol, and even be patient-dependent. In many cases, such a blur can be modeled and predicted with high accuracy providing an important input to a classical deconvolution approach. While traditional deblurring methods tend to be highly noise magnifying, deep learning approaches have the potential to improve upon classic performance limits. However, most network architectures base their restoration on data inputs alone without knowledge of the system blur. In this work, we explore a deep learning approach that takes both image inputs as well as information that characterizes the system blur to combine modeling and deep learning approaches. We apply the approach to CT image restoration and compare with an image-only deep learning approach. We find that inclusion of the system blur model improves deblurring performance - suggesting the potential power of the combined modeling and deep learning technique.

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