Dr. Ye Duan Profile

Dr. Ye Duan

Associate Professor at Univ of Missouri

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Publications (10)

SPIE Journal Paper | 21 December 2022

Contour interpolation by deep learning approach

Chenxi Zhao, Ye Duan, Deshan Yang

JMI, Vol. 9, Issue 06, 064003, (December 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.6.064003

KEYWORDS: Interpolation, Deep learning, Education and training, Contour modeling, Lung, Stomach, Liver, Image segmentation, Data modeling, Kidney

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PurposeContour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a generic deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases.ApproachA generic deep contour interpolation model was developed and trained using 16,796 publicly available cases from 5 different data libraries, covering 15 organs. The network inputs were a 128 × 128 × 5 image patch and the two-dimensional contour masks for the top and bottom slices of the patch. The outputs were the organ masks for the three middle slices. The performance was evaluated on both dice scores and distance-to-agreement (DTA) values.ResultsThe deep contour interpolation model achieved a dice score of 0.95 ± 0.05 and a mean DTA value of 1.09 ± 2.30 mm, averaged on 3167 testing cases of all 15 organs. In a comparison, the results by the conventional SBI method were 0.94 ± 0.08 and 1.50 ± 3.63 mm, respectively. For the difficult cases, the dice score and DTA value were 0.91 ± 0.09 and 1.68 ± 2.28 mm by the deep interpolator, compared with 0.86 ± 0.13 and 3.43 ± 5.89 mm by SBI. The t-test results confirmed that the performance improvements were statistically significant (p < 0.05) for all cases in dice scores and for small organs and difficult cases in DTA values. Ablation studies were also performed.ConclusionsA deep learning method was developed to enhance the process of contour interpolation. It could be useful for expediting the tasks of manual segmentation of organs and structures in the medical images.

Proceedings Article | 22 May 2018 Presentation + Paper

Feasibility of an interpretability metric for LIDAR data

Ye Duan, John Irvine, Hua-mei Chen, Genshe Chen, Erik Blasch, James Nagy

Proceedings Volume 10645, 1064506 (2018) https://doi.org/10.1117/12.2305960

KEYWORDS: LIDAR, Image quality, Clouds, Image sensors, Ranging, Associative arrays, Object recognition, Sensors, Radar imaging, Visible radiation

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SPIE Journal Paper | 21 March 2014

Farmland detection in synthetic aperture radar images with texture signature

Wentao Xu, Guixu Zhang, Ye Duan

JARS, Vol. 8, Issue 01, 084997, (March 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.084997

KEYWORDS: Image segmentation, Synthetic aperture radar, Fuzzy logic, Wavelets, Agriculture, Image filtering, Convolution, Gaussian filters, Receivers, Quantization

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Proceedings Article | 9 June 2011 Paper

Lidar depth image compression using clustering, re-indexing, and JPEG2000

Dmitriy Karpman, David Ashbrook, Xiaoling Li, Ye Duan, Wenjun Zeng

Proceedings Volume 8037, 80370G (2011) https://doi.org/10.1117/12.883656

KEYWORDS: Image compression, LIDAR, JPEG2000, Clouds, Reconstruction algorithms, Image processing, Associative arrays, Analytical research, Technetium, Algorithms

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Proceedings Article | 1 June 2011 Paper

Virtual navigation of interior structures by lidar

Yongjian Xi, Xiaoling Li, Ye Duan, Norbert Maerz

Proceedings Volume 8037, 80371E (2011) https://doi.org/10.1117/12.883628

KEYWORDS: LIDAR, Image segmentation, Visualization, Data modeling, Reconstruction algorithms, Principal component analysis, Clouds, Prototyping, Software development, Algorithm development

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Proceedings Article | 9 March 2010 Paper

Statistical 3D shape analysis of gender differences in lateral ventricles

Qing He, Dmitriy Karpman, Ye Duan

Proceedings Volume 7626, 762609 (2010) https://doi.org/10.1117/12.839984

KEYWORDS: Shape analysis, 3D modeling, Brain, Statistical analysis, Image segmentation, Neurological disorders, 3D image processing, Magnetic resonance imaging, Analytical research, Alzheimer's disease

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Proceedings Article | 9 March 2010 Paper

Shape analysis of corpus callosum in phenylketonuria using a new 3D correspondence algorithm

Qing He, Shawn Christ, Kevin Karsch, Dawn Peck, Ye Duan

Proceedings Volume 7626, 76260A (2010) https://doi.org/10.1117/12.843237

KEYWORDS: Shape analysis, Detection and tracking algorithms, Control systems, 3D acquisition, 3D modeling, Brain, Spherical lenses, Magnetic resonance imaging, Molybdenum, Neuroimaging

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Proceedings Article | 28 February 2009 Paper

Shape analysis of corpus callosum in autism subtype using planar conformal mapping

Qing He, Ye Duan, Xiaotian Yin, Xianfeng Gu, Kevin Karsch, Judith Miles

Proceedings Volume 7262, 726217 (2009) https://doi.org/10.1117/12.812285

KEYWORDS: Shape analysis, 3D modeling, Control systems, Brain, Brain mapping, Image segmentation, Magnetic resonance imaging, Sensors, Data modeling, Visualization

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Proceedings Article | 13 March 2006 Paper

Integrated modeling of PET and DTI information based on conformal brain mapping

Guangyu Zou, Yongjian Xi, Greg Heckenburg, Ye Duan, Jing Hua, Xiangfeng Gu

Proceedings Volume 6143, 614321 (2006) https://doi.org/10.1117/12.653762

KEYWORDS: Brain, Positron emission tomography, Brain mapping, Data modeling, Diffusion tensor imaging, Magnetic resonance imaging, Optical spheres, Neuroimaging, Spherical lenses, Information fusion

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Proceedings Article | 17 January 2005 Paper

A new modeling algorithm for implicit surface polygonalization

Yongjian Xi, Greg Heckenberg, Ye Duan, Hongkai Zhao

Proceedings Volume 5675, (2005) https://doi.org/10.1117/12.585678

KEYWORDS: Data modeling, Optimization (mathematics), Process modeling, Algorithms, Visual process modeling, Optical spheres, Head, Neck, Mathematical modeling, Computer science

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Showing 5 of 10 publications

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