Ms. Qiyuan Hu Profile

Qiyuan Hu

Doctoral Candidate

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Author

Publications (13)

SPIE Journal Paper | 2 May 2024 Open Access

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal

Lindsay Douglas, Jordan Fuhrman, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Hiroyuki Abe, Maryellen Giger

JMI, Vol. 11, Issue 03, 034501, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.034501

KEYWORDS: Breast, Image segmentation, Education and training, Visualization, Magnetic resonance imaging, Tumors, Statistical analysis, Binary data, Image classification, Breast cancer

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PurposeCurrent clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.ApproachA U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings.ResultsStatistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points.ConclusionsResults demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

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SPIE Journal Paper | 20 November 2023 Open Access

U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging

Lindsay Douglas, Roma Bhattacharjee, Jordan Fuhrman, Karen Drukker, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Maryellen Giger

JMI, Vol. 10, Issue 06, 064502, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064502

KEYWORDS: Image segmentation, Breast, 3D image processing, 3D imaging standards, Magnetic resonance imaging, Education and training, Cross validation, 3D modeling, 3D image enhancement, Artificial intelligence

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SPIE Journal Paper | 21 August 2023 Open Access

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Hui Li, Karen Drukker, Qiyuan Hu, Heather Whitney, Jordan Fuhrman, Maryellen Giger

JMI, Vol. 10, Issue 04, 044504, (August 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044504

KEYWORDS: COVID 19, Chest imaging, Data modeling, Deep learning, Education and training, Performance modeling, Radiography, Medical imaging, Machine learning, Diseases and disorders

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SPIE Journal Paper | 1 October 2021 Open Access

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Issam El Naqa, Qiyuan Hu, Weijie Chen, Hui Li, Jordan Fuhrman, Naveena Gorre, Maryellen Giger

JMI, Vol. 8, Issue S1, 010902, (October 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.010902

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SPIE Journal Paper | 28 September 2021 Open Access

Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19

Qiyuan Hu, Karen Drukker, Maryellen Giger

JMI, Vol. 8, Issue S1, 014503, (September 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.014503

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Proceedings Article | 15 February 2021 Presentation + Paper

Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI

Roma Bhattacharjee, Lindsay Douglas, Karen Drukker, Qiyuan Hu, Jordan Fuhrman, Deepa Sheth, Maryellen Giger

Proceedings Volume 11597, 115970D (2021) https://doi.org/10.1117/12.2581846

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Image processing algorithms and systems, Feature extraction, Volume rendering, Medical imaging, Fuzzy logic, Convolutional neural networks, Computer aided diagnosis and therapy

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Proceedings Article | 15 February 2021 Presentation + Paper

Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning

Qiyuan Hu, Karen Drukker, Maryellen Giger

Proceedings Volume 11597, 1159704 (2021) https://doi.org/10.1117/12.2581977

KEYWORDS: Chest imaging, Tissues, Image classification, Data modeling, Radiography, Chest, Databases, Lung, Computer aided diagnosis and therapy

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SPIE Journal Paper | 24 August 2020

Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging

Qiyuan Hu, Heather Whitney, Maryellen Giger

JMI, Vol. 7, Issue 04, 044502, (August 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.4.044502

KEYWORDS: Magnetic resonance imaging, Diffusion weighted imaging, Feature extraction, Breast cancer, Breast, Diagnostics, Computer aided diagnosis and therapy, Tissues, Feature selection, Databases

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SPIE Journal Paper | 30 June 2020 Open Access

Deep-learning-based model observer for a lung nodule detection task in computed tomography

Hao Gong, Qiyuan Hu, Andrew Walther, Chi Wan Koo, Edwin A. Takahashi, David Levin, Tucker Johnson, Megan Hora, Shuai Leng, Joel Fletcher, Cynthia McCollough, Lifeng Yu

JMI, Vol. 7, Issue 04, 042807, (June 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.4.042807

KEYWORDS: Lung, Computed tomography, Molybdenum, Calibration, Image quality, X-ray computed tomography, Solids, Chest, Glasses

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Proceedings Article | 16 March 2020 Presentation + Paper

Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI

Qiyuan Hu, Heather Whitney, Maryellen Giger

Proceedings Volume 11314, 1131411 (2020) https://doi.org/10.1117/12.2548872

KEYWORDS: Magnetic resonance imaging, Feature extraction, Computer aided diagnosis and therapy, Breast, Breast cancer, Neural networks, Image classification, Imaging systems, Machine learning, Computer vision technology, Artificial intelligence

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Proceedings Article | 13 March 2019 Paper

Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer

Qiyuan Hu, Heather Whitney, Alexandra Edwards, John Papaioannou, Maryellen Giger

Proceedings Volume 10950, 109504A (2019) https://doi.org/10.1117/12.2512626

KEYWORDS: Diffusion weighted imaging, Breast, Magnetic resonance imaging, Computer aided diagnosis and therapy, Breast cancer, Feature extraction, Diffusion, RGB color model, Databases, Machine learning, CAD systems

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Proceedings Article | 4 March 2019 Presentation + Paper

Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT

Hao Gong, Andrew Walther, Qiyuan Hu, Chi Wan Koo, Edwin Takahashi, David Levin, Tucker Johnson, Megan Hora, Shuai Leng, J. Fletcher, Cynthia McCollough, Lifeng Yu

Proceedings Volume 10952, 109520K (2019) https://doi.org/10.1117/12.2513451

KEYWORDS: Lung, Chest, Computed tomography, X-ray computed tomography, Image quality, Performance modeling, Reconstruction algorithms, Statistical modeling

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Proceedings Article | 9 March 2017 Paper

A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT

Lifeng Yu, Qiyuan Hu, Chi Wan Koo, Edwin Takahashi, David Levin, Tucker Johnson, Megan Hora, Shane Dirks, Baiyu Chen, Kyle McMillan, Shuai Leng, J. Fletcher, Cynthia McCollough

Proceedings Volume 10132, 101321R (2017) https://doi.org/10.1117/12.2255593

KEYWORDS: Lung imaging, Computed tomography, Lung cancer, Image quality, Performance modeling, Lung

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Showing 5 of 13 publications

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