Medical-image Analysis and Statistical Interpretation (MASI) Lab

Welcome to the MASI Lab

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Recent Publications

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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


Figure2_MI2017_1

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


QBI glyphs in crossing fiber (left) and single fiber (right) regions, for various b-values, number of gradient directions, and maximum SH order fit. Glyphs are shown min-max normalized and are displayed on top of fractional anisotropy maps.

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Kurt G. Schilling, Vishwesh Nath, Justin Blaber Prasanna Parvathaneni, Adam W. Anderson, Bennett A. Landman. “Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging” Magnetic Resonance Imaging. Submitted November 2016. (In Press). Full Text: https://www.ncbi.nlm.nih.gov/pubmed/28438712 Abstract Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used...... KEEP READING

Posted on Friday, May 5th, 2017 in Crossing Fibers, Diffusion Weighted MRI, Neuroimaging, Reproducibility | Comments Off on Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging


Results

Neurite orientation dispersion and density imaging (NODDI) of the prefrontal cortex in psychosis

“Neurite orientation dispersion and density imaging (NODDI) of the prefrontal cortex in psychosis” Authors: Neil D. Woodward, Prasanna Parvatheni, Baxter Rogers, Stephen Damon, Bennett Landman. Society of Biological Psychiatry 2017 (SoBP) Healthy>Psychosis results in ODI... KEEP READING

Posted on Wednesday, February 8th, 2017 in News | Comments Off on Neurite orientation dispersion and density imaging (NODDI) of the prefrontal cortex in psychosis


Example NODDI contrasts

Dendritic organization within the PFC measured in vivo in psychosis using neurite orientation dispersion and density imaging (NODDI)

“Dendritic organization within the PFC measured in vivo in psychosis using neurite orientation dispersion and density imaging (NODDI)” Authors: Neil D. Woodward1, Prasanna Parvatheni2, Baxter Rogers3, Stephen Damon2, Bennett Landman2 .1 Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine 2 School of Engineering, Vanderbilt University 3 Vanderbilt University Institute of Imaging Sciences...... KEEP READING

Posted on Saturday, February 4th, 2017 in News | Comments Off on Dendritic organization within the PFC measured in vivo in psychosis using neurite orientation dispersion and density imaging (NODDI)


Illustration of the differences in model fit for the gold standard datasets with: (A) PASMRI – 1000 s/mm2, (B) Q- ball – 1000 s/mm2, (C) PASMRI – 3000 s/mm2, and (D) Q-ball – 3000 s/mm2.

Comparison of Multi-Fiber Reproducibility of PAS-MRI and Q-ball With Empirical Multiple b-Value HARDI

Vishwesh Nath, Kurt G. Schilling, Justin Blaber, Zhaohua Ding, Adam W. Anderson, Bennett A Landman. “Comparison of Multi-Fiber Reproducibility of PAS-MRI and Q-ball With Empirical Multiple b-Value HARDI ” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Crossing fibers are prevalent in human brains and a...... KEEP READING

Posted on Friday, February 3rd, 2017 in News | Comments Off on Comparison of Multi-Fiber Reproducibility of PAS-MRI and Q-ball With Empirical Multiple b-Value HARDI


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Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning

Jiaqi Liu, Yuankai Huo, Zhoubing Xu, Albert Assad, Richard G. Abramson, Bennett A. Landman. “Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Multi-atlas segmentation has shown to be a promising approach for spleen segmentation. To deal with...... KEEP READING

Posted on Friday, February 3rd, 2017 in News | Comments Off on Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning


Qualitative segmentation results.

Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy

Andrew J. Plassard, Maureen McHugo, Stephan Heckers, Bennett A. Landman. “Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Full Text: Abstract The hippocampus is one of the most studied regions of the brain. Recent advances in MRI have produced high-contrast imaging of the hippocampus....... KEEP READING

Posted on Friday, February 3rd, 2017 in News | Comments Off on Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy


Throughput analysis for each of the test scenarios. (A) presents the
number of datasets processed per minute by each of the scenarios as a function
of the number of datasets selected for processing. (B) shows the fraction of
time spent on overhead relative to the number of datasets.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

Shunxing Bao, Andrew Plassard, Bennett Landman and Aniruddha Gokhale. “Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.”  IEEE International Conference on Cloud Engineering (IC2E), Vancouver, Canada, April 2017. Full text: NIHMSID Abstract Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval....... KEEP READING

Posted on Tuesday, November 15th, 2016 in Big Data, Cloud Computing, Informatics / Big Data | Tags: , Comments Off on Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service


Schematized figure of the tasks performed in the MR Scan.

Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks

Victoria Villalta-Gil, Kendra E Hinton, Bennett A Landman, Benjamin C Yvernault, Scott F Perkins, Allison S Katsantonis, Courtney L Sellani, Benjamin B Lahey, David H Zald. “Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks.” NeuroImage. In Press November 2016 Open Data Resource: https://www.nitrc.org/projects/amygdalamapping Full text: NIHMSID Abstract...... KEEP READING

Posted on Monday, November 14th, 2016 in Neuroimaging, Reproducability | Tags: Comments Off on Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks


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Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation

Andrew J. Plassard, Pierre F. D’Haese, Srivatsan Pallavaram, Allen T. Newton, Daniel O. Claassen, Benoit M. Dawant, Bennett A. Landman. “Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract The basal ganglia and limbic system comprise a relevant network for Parkinson’s...... KEEP READING

Posted on Tuesday, November 1st, 2016 in Neuroimaging | Comments Off on Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation


Hadoop and SGE data retrieval, processing and storage working flow basing on Multi-atlas CRUISE (MaCRUISE) segmentation [14, 15]. The data in an HBase table is approximately balanced to each Regionserver. The Regionserver collocates with a Hadoop Datanode to fully utilize the data collocation and locality[7]. We design our proposed computation models using only the map phase of Hadoop’s MapReduce [13]. In this phase, the data is retrieved locally; if the result were moved to reduce phase, more data movement would occur, because the reduce phase does not ensure process local data. Within the map phase, all necessary data is retrieved and saved on a local directory and gets furtherly processed by locally installed binary executables command-line program. After that, the results of processing are uploaded back to HBase. For SGE, the user submits a batch of jobs to a submit host, and this host dispatches the job to execution hosts. Each execution host retrieves the data within a shared NFS and stores the result back to the NFS.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine

Shunxing Bao, Frederick D. Weitendorf, Andrew J. Plassard, Yuankai Huo, Aniruddha Gokhale, Bennett A. Landman. “Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine”. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Traditional large scale processing uses a cluster computer that combines a group of workstation nodes...... KEEP READING

Posted on Tuesday, November 1st, 2016 in Big Data, Cloud Computing | Comments Off on Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine


Example CT and MRI scans (top row) were expertly labeled (center row, lower row) and used in multi-atlas segmentation pipelines.

Structural-Functional Relationships Between Eye Orbital Imaging Biomarkers and Clinical Visual Assessments

Xiuya Yao, Shikha Chaganti, Kunal P. Nabar, Katrina Nelson, Andrew Plassard, Rob L. Harrigan, Louise A. Mawn, Bennett A. Landman. “Structural-Functional Relationships Between Eye Orbital Imaging Biomarkers and Clinical Visual Assessments” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Eye diseases and visual impairment affect millions...... KEEP READING

Posted on Tuesday, November 1st, 2016 in Eye Imaging | Comments Off on Structural-Functional Relationships Between Eye Orbital Imaging Biomarkers and Clinical Visual Assessments


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