Author

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants

Jul. 25, 2022—Kurt G Schilling, Derek Archer, Fang-Cheng Yeh, Francois Rheault, Leon Cai, Colin Hansen, Qi Yang, Andrea Shafer, Susan Resnick, Kimberly R. Pechman, Katherine A. Gifford, Timothy J. Hohman, Angela Jefferson, Adam W Anderson, Hakmook Kang, Bennett A Landman, Aging and white matter microstructure and microstructure: a longitudinal multi-site diffusion MRI study of 1,184 participants. Brain...

Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI

Jul. 25, 2022—Colin B. Hansen, Kurt G. Schilling, Francois Rheault, Susan Resnick, Andrea T. Shafer, Lori L. Beason-Held, Bennett A. Landmƒan. “Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI.” Magnetic Resonance Imaging (2022). Full Text Abstract Diffusion weighted MRI (DW-MRI) harmonization is necessary for multi-site or multi-acquisition studies. Current statistical methods address the need to harmonize...

Generalizing deep learning brain segmentation for skull removal and intracranial measurements

Jul. 25, 2022—Yue Liu, Yuankai Huo, Blake Dewey, Ying Wei, Ilwoo Lyu, Bennett A. Landman ,“Generalizing deep learning brain segmentation for skull removal and intracranial measurements.”Magnetic Resonance Imaging. Volume 88, May 2022, Pages 44-52 Full Text Abstract Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonanceimaging...

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

Oct. 2, 2017—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...

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

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...

Qualitative results comparing multi-atlas segmentation methods with semi-manual segmentation. The red contours represent the spatial location of the semi-manual segmentation. The white color indicates the negative error, in which the estimated segmentation is smaller than the semi-manual reference. The green and purple color outside the red contours indicates the positive error, in which the estimated segmentation is larger than reference.

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

Oct. 31, 2016—Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243 Abstract Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of...

The large-scale cross-sectional framework on 5111 multi-site MR 3D images.

Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI

Oct. 30, 2016—Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E. Cutting, Bennett A. Landman. “Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI”. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016. Oral Presentation. Full text: NIHMSID 826509 Abstract Understanding brain volumetry is essential to understand neurodevelopment...

Fig-1-Flowchart-of-training-a-data-driven-dictionary-of-whole-brain-probabilistic

Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation

Oct. 4, 2015—Yuankai Huo, Katherine Swett, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman. “Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation”, MICCAI MAPPING Workshop, Munich, Germany, October 2015. Full text: https://www.researchgate.net/publication/303483865_Data-driven_Probabilistic_Atlases_Capture_Whole-brain_Individual_Variation Abstract

Flowchart of the proposed method. Given registered atlases with variable qualities, atlas selection and statistical fusion are considered as two necessary steps to obtain a reasonable fusion estimate of the target segmentation. The SIMPLE algorithm implicitly combines these two steps to fusion selected atlases; however, more information can be incorporated to improve the atlas segmentation, and a more advanced fusion technique can be used after the atlases are selected. We propose to (1) extract a probabilistic prior of the target segmentation by context learning to regularize the atlas selection in SIMPLE and (2) use Joint Label Fusion to obtain the final segmentation while characterizing of the correlated errors from among atlases.

SIMPLE Is a Good Idea (and Better with Context Learning)

Sep. 30, 2014—Zhoubing Xu, Andrew J. Asman, Peter L. Shanahan, Richard G. Abramson, Bennett A. Landman. “SIMPLE Is a Good Idea (and Better with Context Learning)”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, MA, September 2014. Full text: PubMed Abstract Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates...

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