Dr. Yin Yin Profile

Dr. Yin Yin

at iCAD Inc

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Author

Publications (8)

Proceedings Article | 24 March 2016 Paper

Workflow improvements for digital breast tomosynthesis: computerized generation of enhanced synthetic images

Sergei Fotin, Yin Yin, Hrishikesh Haldankar, Jeffrey Hoffmeister, Senthil Periaswamy

Proceedings Volume 9785, 97850K (2016) https://doi.org/10.1117/12.2217182

KEYWORDS: Digital breast tomosynthesis, Computer aided diagnosis and therapy, Reconstruction algorithms, 3D image processing, Computer-aided diagnosis, Detection and tracking algorithms, 3D image reconstruction, 3D image enhancement, Breast, Digital mammography, Deep learning, Convolutional neural networks, Mammography, 3D displays, Image enhancement

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Proceedings Article | 24 March 2016 Paper

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

Sergei Fotin, Yin Yin, Hrishikesh Haldankar, Jeffrey Hoffmeister, Senthil Periaswamy

Proceedings Volume 9785, 97850X (2016) https://doi.org/10.1117/12.2217045

KEYWORDS: Digital breast tomosynthesis, Reconstruction algorithms, Computer aided diagnosis and therapy, Tissues, Computer-aided diagnosis, Neural networks, Mammography, Digital mammography, Detection and tracking algorithms, Evolutionary algorithms, Deep learning, Convolutional neural networks, Breast, Image segmentation, Medical imaging

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Proceedings Article | 14 February 2012 Paper

Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

Yin Yin, Sergei Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, Baris Turkbey, Peter Choyke

Proceedings Volume 8314, 83143B (2012) https://doi.org/10.1117/12.911778

KEYWORDS: Prostate, Image segmentation, Magnetic resonance imaging, 3D image processing, Cancer, Image processing algorithms and systems, Image classification, Prostate cancer, Tissues, Statistical modeling

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Proceedings Article | 14 February 2012 Paper

Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

Yin Yin, Sergei Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, Peter Choyke

Proceedings Volume 8314, 831406 (2012) https://doi.org/10.1117/12.911758

KEYWORDS: Prostate, Image segmentation, Magnetic resonance imaging, Algorithm development, 3D image processing, Image fusion, Detection and tracking algorithms, Image processing, Tissues, 3D modeling

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Proceedings Article | 14 February 2012 Paper

Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

Sergei Fotin, Yin Yin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, Peter Choyke

Proceedings Volume 8314, 83140V (2012) https://doi.org/10.1117/12.911620

KEYWORDS: Prostate, Magnetic resonance imaging, Image segmentation, Image fusion, Image processing, Magnetism, Image registration, Cancer, Convolution, Image filtering

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Proceedings Article | 13 March 2010 Paper

Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

Y. Yin, M. Sonka

Proceedings Volume 7623, 76230W (2010) https://doi.org/10.1117/12.844573

KEYWORDS: Image segmentation, Cartilage, Bone, 3D image processing, Magnetic resonance imaging, Optical spheres, 3D modeling, Associative arrays, Computer engineering, Image processing algorithms and systems

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Proceedings Article | 27 March 2009 Paper

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

Y. Yin, X. Zhang, D. Anderson, T. Brown, C. Van Hofwegen, M. Sonka

Proceedings Volume 7259, 72591O (2009) https://doi.org/10.1117/12.812764

KEYWORDS: Cartilage, Bone, Image segmentation, 3D image processing, Natural surfaces, 3D modeling, Tissues, Data modeling, Medical imaging, Image processing

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We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Proceedings Article | 12 March 2008 Paper

3-D segmentation and quantitative analysis of inner and outer walls of thrombotic abdominal aortic aneurysms

Kyungmoo Lee, Yin Yin, Andreas Wahle, Mark Olszewski, Milan Sonka

Proceedings Volume 6916, 691626 (2008) https://doi.org/10.1117/12.773394

KEYWORDS: Image segmentation, 3D modeling, Quantitative analysis, Computed tomography, Medical imaging, Statistical modeling, 3D image processing, Medicine, Visualization, Surgery

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Showing 5 of 8 publications

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