November, 2016

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

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

Nov. 15, 2016—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....

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

Nov. 14, 2016—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...

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

Nov. 1, 2016—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...

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

Nov. 1, 2016—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...

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

Nov. 1, 2016—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...

Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology

Nov. 1, 2016—Shikha Chaganti, Kunal P. Nabar, Katrina M. Nelson, Louise A. Mawn, Bennett A. Landman. “Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract We examine CT imaging and EMR...

Figure 1. LEFT: Image of the gradient field mapping phantom. RIGHT: Measured field map for the X gradient coil. Fields were measured for all three linear gradient coils.

Stability of Gradient Field Corrections for Quantitative Diffusion MRI

Nov. 1, 2016—Baxter P. Rogers, Justin Blaber, E. Brian Welch, Zhaohua Ding, Adam W. Anderson, Bennett A. Landman. “Stability of Gradient Field Corrections for Quantitative Diffusion MRI” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Full Text: Abstract In diffusion-weighted MRI, gradient nonlinearity causes significant bias in the estimation of quantitative diffusion parameters...

Effects of b-Value and Number of Gradient Directions on Diffusion MRI Measures Obtained with Q-ball Imaging

Nov. 1, 2016—Kurt G. Schilling, Vishwesh Nath, Justin Blaber, Robert L. Harrigan, Zhaohua Ding, Adam W. Anderson, Bennett A Landman. “Effects of b-Value and Number of Gradient Directions on Diffusion MRI Measures Obtained with Q-ball Imaging” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract High-angular-resolution diffusion-weighted imaging (HARDI)...

Flow chart of the four different MAS pipelines. Pipeline 1 conducts the label fusion without atlas selection. Pipeline 2 conducts the atlas selection using the SIMPLE statistical selection method. Pipeline 3 selects atlases which have the closer L to the target image. Pipeline 4 is the L-SIMPLE method, which combines the advantages in Pipelines 2 and 3. A spatial prior is derived using L to guide the statistical atlas selection procedure.

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

Nov. 1, 2016—Yuankai Huo, Jiaqi Liu, Zhoubing Xu, Robert L. Harrigan, Albert Assad, Richard G. Abramson, Bennett A. Landman. “Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly” 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...

DAX – The Next Generation: Towards One Million Processes on Commodity Hardware

Nov. 1, 2016—Stephen M. Damon, Brian D. Boyd, Andrew J. Plassard, Warren Taylor, Bennett A. Landman. “DAX – The Next Generation: Towards One Million Processes on Commodity Hardware” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Large-scale image processing demands a standardized way of storage and distribution. The...