Dr. Jiamin Liu Profile

Dr. Jiamin Liu

Staff Scientist at National Institutes of Health

SPIE Involvement:

Author

Publications (26)

Proceedings Article | 13 March 2019 Presentation + Paper

A semi-supervised CNN learning method with pseudo-class labels for vascular calcification detection on low dose CT scans

Jiamin Liu, Jianhua Yao, Mohammadhadi Bagheri, Ronald Summers

Proceedings Volume 10950, 109501L (2019) https://doi.org/10.1117/12.2513228

KEYWORDS: Computed tomography, Data modeling, Image segmentation, Arteries, Medical imaging, Neural networks, Convolutional neural networks, Machine vision, Computer vision technology, Heart

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Proceedings Article | 27 February 2018 Paper

A coarse-to-fine approach for pericardial effusion localization and segmentation in chest CT scans

Jiamin Liu, Karthik Chellamuthu, Le Lu, Mohammadhadi Bagheri, Ronald Summers

Proceedings Volume 10575, 105753B (2018) https://doi.org/10.1117/12.2295972

KEYWORDS: Image segmentation, Computed tomography, 3D modeling, Convolutional neural networks, Chest, Heart, 3D metrology, Performance modeling, Data modeling, Lutetium

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Proceedings Article | 3 March 2017 Presentation + Paper

Pelvic artery calcification detection on CT scans using convolutional neural networks

Jiamin Liu, Le Lu, Jianhua Yao, Mohammadhadi Bagheri, Ronald Summers

Proceedings Volume 10134, 101341A (2017) https://doi.org/10.1117/12.2255247

KEYWORDS: Arteries, Computed tomography, Neural networks, Kidney, Machine learning, Convolutional neural networks, Radiology, Image segmentation, Computer aided diagnosis and therapy, Image processing, Network architectures, Lutetium

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Proceedings Article | 24 March 2016 Paper

Colitis detection on abdominal CT scans by rich feature hierarchies

Jiamin Liu, Nathan Lay, Zhuoshi Wei, Le Lu, Lauren Kim, Evrim Turkbey, Ronald Summers

Proceedings Volume 9785, 97851N (2016) https://doi.org/10.1117/12.2217681

KEYWORDS: Convolutional neural networks, Colon, Computed tomography, Inflammation, Feature extraction, Image segmentation, Computer aided diagnosis and therapy, Image processing, Lutetium, Radiology

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SPIE Journal Paper | 30 December 2015

Automated segmentation of the thyroid gland on thoracic CT scans by multiatlas label fusion and random forest classification

Divya Narayanan, Jiamin Liu, Lauren Kim, Kevin Chang, Le Lu, Jianhua Yao, Evrim Turkbey, Ronald Summers

JMI, Vol. 2, Issue 04, 044006, (December 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.4.044006

KEYWORDS: Image restoration, Image segmentation, Computed tomography, Image fusion, Image registration, Chest, Tissues, Image analysis, Imaging systems, Detection and tracking algorithms

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Proceedings Article | 20 March 2015 Paper

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

Jiamin Liu, Kevin Chang, Lauren Kim, Evrim Turkbey, Le Lu, Jianhua Yao, Ronald Summers

Proceedings Volume 9414, 941413 (2015) https://doi.org/10.1117/12.2082204

KEYWORDS: Image segmentation, Computed tomography, Image fusion, Radiotherapy, Image processing algorithms and systems, Detection and tracking algorithms, Image registration, Image classification, Neck, Head

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Proceedings Article | 20 March 2015 Paper

Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

Joanne Hoffman, Jiamin Liu, Evrim Turkbey, Lauren Kim, Ronald Summers

Proceedings Volume 9414, 94141R (2015) https://doi.org/10.1117/12.2082190

KEYWORDS: Lymphatic system, Computed tomography, Image segmentation, Lung cancer, Lung, Spine, Esophagus, Image registration, Radiology, Veins

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Proceedings Article | 21 March 2014 Paper

Robust automated lymph node segmentation with random forests

David Allen, Le Lu, Jianhua Yao, Jiamin Liu, Evrim Turkbey, Ronald Summers

Proceedings Volume 9034, 90343X (2014) https://doi.org/10.1117/12.2043926

KEYWORDS: Lymphatic system, Image segmentation, Computed tomography, Detection and tracking algorithms, Medical imaging, 3D image processing, Tissues, Feature selection, Receivers, Feature extraction

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Proceedings Article | 20 March 2014 Paper

Reducing false positives of small bowel segmentation on CT scans by localizing colon regions

Weidong Zhang, Jiamin Liu, Jianhua Yao, Ronald Summers

Proceedings Volume 9035, 90350Z (2014) https://doi.org/10.1117/12.2043344

KEYWORDS: Colon, Image segmentation, Arteries, Computed tomography, Volume rendering, Sensors, Computer aided diagnosis and therapy, Tumors, Feature extraction, Virtual colonoscopy

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Proceedings Article | 18 March 2014 Paper

Mediastinal lymph node detection on thoracic CT scans using spatial prior from multi-atlas label fusion

Jiamin Liu, Jocelyn Zhao, Joanne Hoffman, Jianhua Yao, Weidong Zhang, Evrim Turkbey, Shijun Wang, Christine Kim, Ronald Summers

Proceedings Volume 9035, 90350M (2014) https://doi.org/10.1117/12.2043737

KEYWORDS: Lymphatic system, Image segmentation, Computed tomography, Lung, Esophagus, Heart, Chest, 3D image processing, Feature extraction, Spine

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Proceedings Article | 11 March 2014 Paper

Automatic segmentation of abdominal vessels for improved pancreas localization

Amal Farag, Jiamin Liu, Ronald Summers

Proceedings Volume 9037, 90371M (2014) https://doi.org/10.1117/12.2043867

KEYWORDS: Pancreas, Veins, Image segmentation, Liver, Spleen, Tissues, Computed tomography, Automatic tracking, Abdomen, Kidney

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Proceedings Article | 18 March 2013 Paper

Segmenting the thoracic, abdominal and pelvic musculature on CT scans combining atlas-based model and active contour model

Weidong Zhang, Jiamin Liu, Jianhua Yao, Ronald Summers

Proceedings Volume 8670, 867008 (2013) https://doi.org/10.1117/12.2007970

KEYWORDS: Image segmentation, Computed tomography, Bone, 3D modeling, Computer aided diagnosis and therapy, Spine, Abdomen, Tissues, Solid modeling, Tumors

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Proceedings Article | 23 February 2012 Paper

Computer-aided marginal artery detection on computed tomographic colonography

Zhuoshi Wei, Jianhua Yao, Shijun Wang, Jiamin Liu, Ronald Summers

Proceedings Volume 8315, 83150Z (2012) https://doi.org/10.1117/12.911850

KEYWORDS: Arteries, Colon, Computer aided diagnosis and therapy, Tomography, Image segmentation, Blood vessels, Blood, Computer aided design, Shape memory alloys

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Proceedings Article | 23 February 2012 Paper

Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients

Jiamin Liu, Jeremy Hua, Vivek Chellappa, Nicholas Petrick, Berkman Sahiner, Mohammed Farooqui, Gerald Marti, Adrian Wiestner, Ronald Summers

Proceedings Volume 8315, 83150B (2012) https://doi.org/10.1117/12.911836

KEYWORDS: Lymphatic system, Computed tomography, Image segmentation, Lung, Leukemia, Lithium, 3D modeling, 3D image processing, Feature extraction, Computer aided diagnosis and therapy

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Proceedings Article | 23 February 2012 Paper

Computer-aided mesenteric small vessel segmentation on high-resolution 3D contrast-enhanced CT angiography scans

Weidong Zhang, Jiamin Liu, Jianhua Yao, Tan Nguyen, Adeline Louie, Stephen Wank, Ronald Summers

Proceedings Volume 8315, 83151T (2012) https://doi.org/10.1117/12.911841

KEYWORDS: Image segmentation, Computed tomography, Angiography, Abdomen, Arteries, Image filtering, Tumors, Bone, 3D image processing, Blood vessels

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Proceedings Article | 5 March 2011 Paper

Computer-aided abdominal lymph node detection using contrast-enhanced CT images

Jiamin Liu, Jeremy Hua, Jianhua Yao, Jacob White, Ronald Summers

Proceedings Volume 7963, 796313 (2011) https://doi.org/10.1117/12.878252

KEYWORDS: Lymphatic system, Image segmentation, Computed tomography, Spine, 3D image processing, Blood vessels, Blob detection, Computer aided diagnosis and therapy, Image filtering, 3D modeling

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Proceedings Article | 9 March 2010 Paper

Computer-aided lymph node detection in abdominal CT images

Jiamin Liu, Jacob White, Ronald Summers

Proceedings Volume 7624, 76240U (2010) https://doi.org/10.1117/12.844406

KEYWORDS: Lymphatic system, Image segmentation, Computed tomography, 3D image processing, Computer aided diagnosis and therapy, Sensors, Blood vessels, Image filtering, 3D modeling, Arteries

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Proceedings Article | 3 March 2009 Paper

High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

Jiamin Liu, Shijun Wang, Suraj Kabadi, Ronald Summers

Proceedings Volume 7260, 72601B (2009) https://doi.org/10.1117/12.811654

KEYWORDS: CAD systems, Virtual colonoscopy, Computer aided diagnosis and therapy, Image segmentation, Signal attenuation, Computed tomography, Colon, Diffusion, Tissues, Colorectal cancer

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CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.

Proceedings Article | 8 March 2007 Paper

Oriented active shape models for 3D segmentation in medical images

Jiamin Liu, Jayaram Udupa

Proceedings Volume 6512, 65122H (2007) https://doi.org/10.1117/12.710602

KEYWORDS: 3D modeling, Image segmentation, 3D image processing, Medical imaging, Magnetic resonance imaging, Breast, Statistical analysis, Computer programming, Data modeling, Binary data

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Proceedings Article | 13 March 2006 Paper

An intensity standardization-based method for inhomogeneity correction in MRI

Ying Zhuge, Jayaram Udupa, Jiamin Liu, Punam Saha

Proceedings Volume 6143, 614324 (2006) https://doi.org/10.1117/12.653734

KEYWORDS: Magnetic resonance imaging, Tissues, Image segmentation, Image processing, Medical imaging, Bismuth, Image analysis, Data modeling, Scanners, Scene simulation

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Proceedings Article | 10 March 2006 Paper

Oriented active shape models

Jiamin Liu, Jayaram Udupa

Proceedings Volume 6144, 614404 (2006) https://doi.org/10.1117/12.653589

KEYWORDS: Image segmentation, Computer programming, Liver, Medical imaging, Image processing, Shape analysis, Computed tomography, Statistical modeling, Bone, Magnetic resonance imaging

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Proceedings Article | 29 April 2005 Paper

Model-based 3D segmentation of the bones of joints in medical images

Jiamin Liu, Jayaram Udupa, Punam Saha, Dewey Odhner, Bruce Hirsch, Sorin Siegler, Scott Simon, Beth Winkelstein

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595425

KEYWORDS: Image segmentation, Bone, Magnetic resonance imaging, 3D image processing, 3D modeling, Spine, 3D displays, Computed tomography, Binary data, Medical imaging

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Proceedings Article | 30 April 2004 Paper

3D stress MRI for studying the functional pathologies of the ankle complex

Jayaram Udupa, Sorin Siegler, Bruce Hirsch, Stacie Ringleb, Enyi Okereke, Neil Roach, Carl Imhauser, Dewey Odhner, Jiamin Liu, Punam Saha

Proceedings Volume 5369, (2004) https://doi.org/10.1117/12.536433

KEYWORDS: Magnetic resonance imaging, Bone, 3D image processing, Kinematics, 3D metrology, Pathology, In vitro testing, Image processing, Image analysis, Atomic layer deposition

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Proceedings Article | 15 May 2003 Paper

Membership-based multiprotocol MR brain image segmentation

Ying Zhuge, Jiamin Liu, Jayaram Udupa

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481410

KEYWORDS: Tissues, Brain, Image segmentation, Magnetic resonance imaging, Neuroimaging, Fuzzy logic, Curium, Image processing, Medical imaging, Expectation maximization algorithms

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Proceedings Article | 15 May 2003 Paper

Model-based 3D segmentation of the bones of the ankle and subtalar joints in MR images

Jiamin Liu, Jayaram Udupa, Punam Saha, Dewey Odhner, Bruce Hirsch, Sorin Siegler

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.483574

KEYWORDS: Image segmentation, Bone, 3D modeling, Magnetic resonance imaging, 3D image processing, Kinematics, Binary data, Medical imaging, Tissues, Image processing

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Proceedings Article | 9 May 2002 Paper

Scale-based method for correcting background intensity variation in acquired images

Ying Zhuge, Jayaram Udupa, Jiamin Liu, Punam Saha, Tad Iwanage

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467067

KEYWORDS: Image segmentation, Magnetic resonance imaging, Medical imaging, Imaging devices, Surgery, Neodymium, Image processing, Image analysis, Tissues, Scanners

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Showing 5 of 26 publications

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