Dr. Maosheng Xu Profile

Dr. Maosheng Xu

at First Affiliated Hospital of Zhejiang Chinese Medical Univ.

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Publications (4)

Proceedings Article | 2 March 2020 Paper

Deep super-resolution network on diffusion weighted imaging for improving prediction of histological grade in breast cancer

Lihua Li, Zuhui Liu, Ming Fan, Maosheng Xu, Shiwei Wang

Proceedings Volume 11318, 113180H (2020) https://doi.org/10.1117/12.2548550

KEYWORDS: Breast cancer, Diffusion weighted imaging, Super resolution, Image segmentation, Tumors, Magnetic resonance imaging, Image resolution, Breast, Bismuth, Statistical analysis

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In many medical image applications, high-resolution images are needed to facilitate early diagnosis. However, due to technical limitations, it may not be easy to obtain an image with ideal resolution especially for the diffusion weighted imaging (DWI). Super-resolution (SR) technology is developed to solve this problem by generating high-resolution (HR) images from low-resolution (LR) images. The purpose of this study is to obtain the SR-DWI from the original LR image through deep super-resolution network. The effectiveness of the SR image is assessed by radiomic analysis in predicting the histological grade of breast cancer. To this end, a dataset of 144 breast cancer cases were collected, including 83 cases who diagnosed as high-grade malignant (Grade 3) breast cancer, and 61 who were median-grade malignant (Grade 2). For each case, the dynamic enhanced magnetic resonance imaging (DCE-MRI), and the apparent diffusion coefficients (ADC) map derived from DWI were obtained. Lesion segmentation was performed on each of the original ADC and the SR-ADC, in which 30 texture and 10 statistical features were extracted. Deep SR model was established by an end-to-end training from the LR DCE-MRI and the HR counterparts and was applied to the ADC images to obtain SR-ADCs. Univariate and multivariate logistic regression classifier was implemented to evaluate the performance of the individual feature and collective features, respectively. The model performance was evaluated by the area under the curve (AUC) under leave one-out cross-validation (LOOCV). For the individual feature analysis, the performance in terms of AUC was significantly better based on the SR-ADC image than that based on the original ADC image. For multivariate analysis, the classifier performance in terms of AUCs were 0.848±0.061 and 0.878±0.051 for the original ADC and the SR ADC, respectively. The results suggested that the enhanced resolution of ADC image had the potential to more accurately predict histological grade in breast cancer.

Proceedings Article | 15 March 2019 Paper

Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features

Lihua Li, Wenrui Zhao, Ming Fan, Maosheng Xu

Proceedings Volume 10954, 109541H (2019) https://doi.org/10.1117/12.2513126

KEYWORDS: Diffusion weighted imaging, Breast cancer, Magnetic resonance imaging, Breast, Tumors, Image segmentation, Image enhancement, Cancer, Image fusion, Statistical analysis

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Proceedings Article | 15 March 2019 Paper

Heterogeneity of tumor and its surrounding stroma on DCE-MRI and diffusion weighted imaging in predicting histological grade and lymph node status of breast cancer

Lihua Li, Qingmei Chen, Peng Zhang, Ming Fan, Maosheng Xu

Proceedings Volume 10954, 109541G (2019) https://doi.org/10.1117/12.2513095

KEYWORDS: Tumors, Lymphatic system, Breast cancer, Diffusion weighted imaging, Image analysis, Statistical analysis, Magnetic resonance imaging, Feature extraction, Breast

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Proceedings Article | 13 March 2019 Presentation + Paper

DCE-MRI based analysis of intratumor heterogeneity by decomposing method for prediction of HER2 status in breast cancer

Ming Fan, Maosheng Xu, Yuanzhe Li, Lihua Li, Peng Zhang

Proceedings Volume 10950, 109500H (2019) https://doi.org/10.1117/12.2513102

KEYWORDS: Tumors, Breast cancer, Magnetic resonance imaging, Breast, Feature extraction, Image segmentation, Content addressable memory, Lithium, Medicine, Fluctuations and noise

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