Mr. Gezheng Wen Profile

Gezheng Wen

Doctoral Candidate at Univ of Texas at Austin

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Author

Publications (8)

SPIE Journal Paper | 17 January 2018

Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images

Gezheng Wen, Ho-chang Chang, Jacob Reinhold, Joseph Lo, Mia Markey

JMI, Vol. 5, Issue 01, 015501, (January 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.015501

KEYWORDS: Digital breast tomosynthesis, Breast, Signal to noise ratio, Mammography, Performance modeling, 3D image processing, Reconstruction algorithms, Visualization, Sensors, 3D modeling

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SPIE Journal Paper | 10 May 2017

Comparative study of computational visual attention models on two-dimensional medical images

Gezheng Wen, Brenda Rodriguez-Niño, Furkan Pecen, David Vining, Naveen Garg, Mia Markey

JMI, Vol. 4, Issue 02, 025503, (May 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.025503

KEYWORDS: Visualization, Performance modeling, Medical imaging, Eye models, Visual process modeling, Data modeling, Computational modeling, Robotics, Medical imaging modalities, Motion estimation

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Proceedings Article | 10 March 2017 Presentation + Paper

Digital breast tomosynthesis for detecting multifocal and multicentric breast cancer: influence of acquisition geometry on model observer performance in breast phantom images

Gezheng Wen, Subok Park, Mia Markey

Proceedings Volume 10136, 101360V (2017) https://doi.org/10.1117/12.2254193

KEYWORDS: Digital breast tomosynthesis, Breast, 3D modeling, Breast cancer, Signal detection, X-rays, Image quality, Signal to noise ratio, 3D image processing, Lab on a chip, Tumors, Magnetic resonance imaging

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Multifocal and multicentric breast cancer (MFMC), i.e., the presence of two or more tumor foci within the same breast, has an immense clinical impact on treatment planning and survival outcomes. Detecting multiple breast tumors is challenging as MFMC breast cancer is relatively uncommon, and human observers do not know the number or locations of tumors a priori. Digital breast tomosynthesis (DBT), in which an x-ray beam sweeps over a limited angular range across the breast, has the potential to improve the detection of multiple tumors.^{1, 2} However, prior efforts to optimize DBT image quality only considered unifocal breast cancers (e.g.,^3-9), so the recommended geometries may not necessarily yield images that are informative for the task of detecting MFMC. Hence, the goal of this study is to employ a 3D multi-lesion (ml) channelized-Hotelling observer (CHO) to identify optimal DBT acquisition geometries for MFMC. Digital breast phantoms and simulated DBT scanners of different geometries (e.g., wide or narrow arc scans, different number of projections in each scan) were used to generate image data for the simulation study. Multiple 3D synthetic lesions were inserted into different breast regions to simulate MF cases and MC cases. 3D partial least squares (PLS) channels, and 3D Laguerre-Gauss (LG) channels were estimated to capture discriminant information and correlations among signals in locally varying anatomical backgrounds, enabling the model observer to make both image-level and location-specific detection decisions. The 3D ml-CHO with PLS channels outperformed that with LG channels in this study. The simulated MC cases and MC cases were not equally difficult for the ml-CHO to detect across the different simulated DBT geometries considered in this analysis. Also, the results suggest that the optimal design of DBT may vary as the task of clinical interest changes, e.g., a geometry that is better for finding at least one lesion may be worse for counting the number of lesions.

Proceedings Article | 10 March 2017 Presentation + Paper

Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: a model observer study with anthropomorphic computational breast phantoms

Jacob Reinhold, Gezheng Wen, Joseph Lo, Mia Markey

Proceedings Volume 10136, 101360W (2017) https://doi.org/10.1117/12.2255909

KEYWORDS: Digital breast tomosynthesis, Breast, Signal to noise ratio, 3D image processing, 3D modeling, Computer simulations, 3D vision, Breast imaging, Digital imaging, Computing systems, Performance modeling, Signal detection, Sensors, Data modeling

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Proceedings Article | 24 March 2016 Paper

Model observer design for detecting multiple abnormalities in anatomical background images

Gezheng Wen, Mia Markey, Subok Park

Proceedings Volume 9787, 97870S (2016) https://doi.org/10.1117/12.2217665

KEYWORDS: Tumor growth modeling, Performance modeling, Image analysis, Medical imaging, Image quality, Lab on a chip, Signal to noise ratio, Mammography, Neptunium

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SPIE Journal Paper | 6 January 2016

Computational assessment of visual search strategies in volumetric medical images

Gezheng Wen, Avigael Aizenman, Trafton Drew, Jeremy Wolfe, Tamara Miner Haygood, Mia K. Markey

JMI, Vol. 3, Issue 01, 015501, (January 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.1.015501

KEYWORDS: Scanners, Visualization, Lung, Computed tomography, Medical imaging, Eye, Chest, Target detection, 3D image processing, Motion models

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Proceedings Article | 17 March 2015 Paper

The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

Gezheng Wen, Mia Markey

Proceedings Volume 9416, 94160Y (2015) https://doi.org/10.1117/12.2081956

KEYWORDS: Medical imaging, Statistical analysis, Computer simulations, Performance modeling, Data modeling, Signal detection, Image quality, Neodymium, Chemical elements, Copper

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Proceedings Article | 11 March 2014 Paper

A stereo matching model observer for stereoscopic viewing of 3D medical images

Gezheng Wen, Mia Markey, Gautam Muralidlhar

Proceedings Volume 9037, 90370Z (2014) https://doi.org/10.1117/12.2043175

KEYWORDS: 3D modeling, Breast, Data modeling, 3D image processing, Visual process modeling, Medical imaging, 3D vision, Performance modeling, Tissues, Computer simulations

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Showing 5 of 8 publications

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