Medical-image Analysis and Statistical Interpretation (MASI) Lab

Welcome to the MASI Lab

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Recent Publications

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TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction

Ilwoo Lyu, Sun Hyung Kim, Neil D. Woodward, Martin A. Styner and Bennett A. Landman. “TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction “. IEEE Transactions on Medical Imaging (2017). Full text: http://dx.doi.org/10.1109/TMI.2017.2787589 Abstract A proper geometric representation of the cortical regions is a fundamental task for cortical shape analysis and landmark extraction....... KEEP READING

Posted on Thursday, December 28th, 2017 in News | Comments Off on TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction


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Learning Implicit Brain MRI Manifolds with Deep Learning

Bermudez, C., Plassard, A.J., Davis, T.L., Newton, A.T., Resnick, S.M., and Landman, B.A. (2017) “Learning implicit brain MRI manifolds with deep learning.” arXiv preprint arXiv:1801.01847 Full Text: https://arxiv.org/pdf/1801.01847.pdf Abstract An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease...... KEEP READING

Posted on Friday, December 22nd, 2017 in Context Learning, Deep Learning, Generative Adversarial Networks, Image Processing, Machine Learning, Noise Estimation | Comments Off on Learning Implicit Brain MRI Manifolds with Deep Learning


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Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation

Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett A. Landman. “Improved Stability of Whole Brain Surface Parcellation with Multi-atlas Segmentation.” SPIE 2018 Full text: https://arxiv.org/abs/1712.00543 Abstract Whole brain segmentation and cortical surface parcellation are essential in understanding the brain’s anatomical-functional relationships. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole...... KEEP READING

Posted on Tuesday, December 19th, 2017 in News | Comments Off on Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation


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Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Andrew J. Plassard, Jiaqi Liu, Yuang Yao, Albert Assad, Richard G.  Abramson, Bennett A. Landman. “Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.” SPIE 2018 Full text: https://arxiv.org/abs/1712.00542 Abstract Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally...... KEEP READING

Posted on Tuesday, December 19th, 2017 in News | Comments Off on Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks


SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods

Citation: ” SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods”. Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling, Allison E. Hainline, Prasanna Parvathaneni,  Justin A. Blaber, Ilwoo Lyu, Adam W. Anderson, Hakmook Kang, Allen T. Newton, Baxter P. Rogers, Bennett A. Landman   In SPIE Medical Imaging, International Society for Optics and Photonics, 2018 (Accepted) Abstract:  High Angular...... KEEP READING

Posted on Tuesday, December 19th, 2017 in News | Comments Off on SHARD: Spherical Harmonic-based Robust Outlier Detection for HARDI Methods


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Constructing Statistically Unbiased Cortical Surface Templates Using Feature Space Covariance

Citation: ” Constructing statistically unbiased cortical surface templates using feature-space covariance”. Prasanna Parvathaneni, Ilwoo Lyu, Justin A. Blaber, Yuankai Huo, Allison E. Hainline, Neil D. Woodward, Hakmook Kang, Bennett A. Landman   In SPIE Medical Imaging, International Society for Optics and Photonics, 2018 (Accepted). Abstract The choice of surface template plays an important role in cross-sectional subject analyses...... KEEP READING

Posted on Tuesday, December 19th, 2017 in Image Processing, Neuroimaging, Registration | Tags: , , , , , Comments Off on Constructing Statistically Unbiased Cortical Surface Templates Using Feature Space Covariance


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Automated Characterization of Pyelocalyceal Anatomy Using CT Urograms to Aid in Management of Kidney Stones

Yuankai Huo, Vaughn Braxton, S. Duke Herrell, Bennett Landman, and Smita De. “Automated Characterization of Pyelocalyceal Anatomy Using CT Urograms to Aid in Management of Kidney Stones.” In Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures, pp. 99-107. Springer, Cham, 2017. Full text: https://link.springer.com/chapter/10.1007/978-3-319-67543-5_9 Abstract Nephrolithiasis is a costly and prevalent disease that is associated...... KEEP READING

Posted on Monday, December 18th, 2017 in News | Comments Off on Automated Characterization of Pyelocalyceal Anatomy Using CT Urograms to Aid in Management of Kidney Stones


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Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly using Multi-atlas Segmentation

Yuankai Huo, Jiaqi Liu, Zhoubing Xu, Robert L. Harrigan, Albert Assad, Richard G. Abramson, and Bennett A. Landman. “Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly using Multi-atlas Segmentation.” IEEE Transactions on Biomedical Engineering (2017). Full text: onlinelibrary.wiley.com/doi/10.1002/hbm.23432/full Abstract Objective: Magnetic resonance imaging (MRI) is an essential imaging modality in non-invasive splenomegaly diagnosis. However, it is...... KEEP READING

Posted on Monday, December 18th, 2017 in News | Comments Off on Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly using Multi-atlas Segmentation


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Opportunities for Mining Radiology Archives for Pediatric Control Images

Bermudez, C., Probst, V. N., Davis, L. T., Lasko, T., & Landman, B. A. (2017). Opportunities for Mining Radiology Archives for Pediatric Control Images. arXiv preprint arXiv:1712.02728. Full Text: https://arxiv.org/ftp/arxiv/papers/1712/1712.02728.pdf Abstract A large database of brain imaging data from healthy, normal controls is useful to describe physiologic and pathologic structural changes at a population scale....... KEEP READING

Posted on Sunday, December 17th, 2017 in Big Data, Computed Tomography, Magnetic resonance imaging | Comments Off on Opportunities for Mining Radiology Archives for Pediatric Control Images


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Sulcal Depth-based Cortical Shape Analysis in Normal Healthy Control and Schizophrenia Groups

Ilwoo Lyu, Hakmook Kang, Neil D. Woodward, and Bennett A. Landman. “Sulcal Depth-based Cortical Shape Analysis in Normal Healthy Control and Schizophrenia Groups”. SPIE Medical Imaging 2018. Abstract Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity...... KEEP READING

Posted on Monday, December 11th, 2017 in News | Comments Off on Sulcal Depth-based Cortical Shape Analysis in Normal Healthy Control and Schizophrenia Groups


Cortical regions‐of‐interest (ROIs) and thalamus for one subject. Segmentations derived from multi atlas were used to create subject‐specific ROIs for the thalamus (purple) and 6 cortical subdivisions: prefrontal cortex (blue), motor cortex/supplementary motor area (red), somatosensory cortex (cyan), temporal cortex (green), posterior parietal cortex (yellow), and occipital cortex (violet). The thalamus and cortical ROIs were used as seed and targets, respectively, to quantify thalamocortical structural connectivity using probabilistic tractography. The lateral surface renderings in the top panel were generated by projecting the cortical ROIs onto the central surface of the cortical mantle.

Prefrontal-Thalamic anatomical connectivity and executive cognitive function in schizophrenia

Monica Giraldo-Chica, Baxter P. Rogers, Stephen M. Damon, Bennett A. Landman, Neil D. Woodward. “Prefrontal-Thalamic anatomical connectivity and executive cognitive function in schizophrenia.” Biological Psychiatry, September 2017. http://dx.doi.org/10.1016/j.biopsych.2017.09.022 Full text: http://www.biologicalpsychiatryjournal.com/article/S0006-3223(17)32010-3/fulltext NIHMSID   Abstract Background Executive cognitive functions, including working memory, cognitive flexibility, and inhibition, are impaired in schizophrenia. Executive functions rely on coordinated information processing...... KEEP READING

Posted on Monday, October 2nd, 2017 in News | Comments Off on Prefrontal-Thalamic anatomical connectivity and executive cognitive function in schizophrenia


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4D Multi-atlas Label Fusion using Longitudinal Images

Yuankai Huo, Susan M. Resnick and Bennett A. Landman. “4D Multi-atlas Label Fusion using Longitudinal Images”. MICCAI Patch-MI Workshop, 2017. Full text: https://drive.google.com/open?id=0Bzzeqiij2Zara1ZlQXJiclM2UEE Abstract Longitudinal reproducibility is an essential concern in automated medical image segmentation, yet has proven to be an elusive objective as manual brain structure tracings have shown more than 10% variability. To improve reproducibility, longitudinal...... KEEP READING

Posted on Tuesday, August 29th, 2017 in Image Segmentation, Label fusion, Neuroimaging | Comments Off on 4D Multi-atlas Label Fusion using Longitudinal Images


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Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Citation: Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer. (2017). Accepted.  Abstract Tract-based spatial statistics (TBSS) has proven to be...... KEEP READING

Posted on Monday, August 28th, 2017 in Image Processing, Image Segmentation, Neuroimaging | Comments Off on Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure


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Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Citation: Gray Matter Surface based Spatial Statistics in Neuroimaging Studies. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstract In this study, we propose gray matter surface based spatial statistics (GS-BSS)...... KEEP READING

Posted on Thursday, June 1st, 2017 in Diffusion Tensor Imaging, Diffusion Weighted MRI, fMRI, Image Processing, Image Segmentation, Neuroimaging, Registration | Comments Off on Gray Matter Surface based Spatial Statistics in Neuroimaging Studies


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Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features

Citation: Bermudez, C. et.al. Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI  Structural Features. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstrract It is well known that there are structural changes that occur in the brain with age. However, there are insufficient imaging biomarkers that reliably describe structural...... KEEP READING

Posted on Monday, May 15th, 2017 in Big Data, Image Processing, Image Segmentation, Machine Learning, Multi-atlas Segmentation, Neuroimaging | Comments Off on Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features


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