Dr. Yefeng Zheng Profile

Dr. Yefeng Zheng

Research Scientist at Siemens Corp Corporate Technology

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Publications (8)

Proceedings Article | 21 March 2014 Paper

Improving accuracy in coronary lumen segmentation via explicit calcium exclusion, learning-based ray detection and surface optimization

Felix Lugauer, Jingdan Zhang, Yefeng Zheng, Joachim Hornegger, B. Michael Kelm

Proceedings Volume 9034, 90343U (2014) https://doi.org/10.1117/12.2043238

KEYWORDS: Arteries, Calcium, Image segmentation, Data modeling, Computer aided diagnosis and therapy, Angiography, Sensors, Databases, Computed tomography, Tissues

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Proceedings Article | 13 March 2013 Paper

Graph cuts based left atrium segmentation refinement and right middle pulmonary vein extraction in C-arm CT

Dong Yang, Yefeng Zheng, Matthias John

Proceedings Volume 8669, 86693U (2013) https://doi.org/10.1117/12.2007137

KEYWORDS: Image segmentation, Veins, Photovoltaics, Computed tomography, Optimization (mathematics), Atrial fibrillation, Data modeling, Motion models, X-ray detectors, Heart

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Proceedings Article | 17 February 2012 Paper

Robust pigtail catheter tip detection in fluoroscopy

Stratis Tzoumas, Peng Wang, Yefeng Zheng, Matthias John, Dorin Comaniciu

Proceedings Volume 8316, 83160M (2012) https://doi.org/10.1117/12.911084

KEYWORDS: Sensors, Fluoroscopy, Visualization, Surgery, 3D modeling, Detection and tracking algorithms, Visibility, Motion models, Visual process modeling, Video

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Proceedings Article | 12 March 2011 Paper

Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes

Yefeng Zheng, Maciej Loziczonek, Bogdan Georgescu, S. Kevin Zhou, Fernando Vega-Higuera, Dorin Comaniciu

Proceedings Volume 7962, 79621K (2011) https://doi.org/10.1117/12.877233

KEYWORDS: Arteries, Heart, Machine learning, Image segmentation, Feature extraction, Computer aided diagnosis and therapy, Computer aided design, Computed tomography, Image classification, Volume visualization

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Proceedings Article | 24 February 2010 Paper

Aortic valve and ascending aortic root modeling from 3D and 3D+t CT

Saša Grbic, Razvan Ionasec, Dominik Zäuner, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu

Proceedings Volume 7625, 76250H (2010) https://doi.org/10.1117/12.844566

KEYWORDS: 3D modeling, Data modeling, Principal component analysis, Sensors, Visualization, Distance measurement, Visual process modeling, 3D metrology, Heart, Optical tracking

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Proceedings Article | 27 March 2009 Paper

Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface

Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Dorin Comaniciu

Proceedings Volume 7259, 72593V (2009) https://doi.org/10.1117/12.811033

KEYWORDS: Blood, Heart, Image segmentation, Computer programming, Principal component analysis, Lithium, Tissues, Medical imaging, Machine learning, Computed tomography

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Proceedings Article | 27 March 2009 Paper

Automatic left ventricle detection in MRI images using marginal space learning and component-based voting

Yefeng Zheng, Xiaoguang Lu, Bogdan Georgescu, Arne Littmann, Edgar Mueller, Dorin Comaniciu

Proceedings Volume 7259, 725906 (2009) https://doi.org/10.1117/12.811045

KEYWORDS: Sensors, Magnetic resonance imaging, Distance measurement, Heart, 3D image processing, Wavelets, Image processing, Error analysis, Stars, Tolerancing

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Proceedings Article | 26 March 2008 Paper

Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

Yefeng Zheng, Bogdan Georgescu, Adrian Barbu, Michael Scheuering, Dorin Comaniciu

Proceedings Volume 6914, 691416 (2008) https://doi.org/10.1117/12.770710

KEYWORDS: Heart, 3D modeling, Image segmentation, Statistical modeling, Data modeling, Process modeling, Systems modeling, Computed tomography, Databases, 3D image processing

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Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.

Showing 5 of 8 publications

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