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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...

Flowchart of the proposed iterative Non-Local STAPLE (iNLS) multi-atlas framework. Using an iterative atlas-target registration refinement framework, we expand the range of anatomical variability that can be reliably segmented.

Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion

Oct. 30, 2013—Andrew J. Asman, Seth A. Smith, Daniel Reich, and Bennett A. Landman. “Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion”, In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2013 Nagoya, Japan Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918679/ Abstract New magnetic resonance imaging (MRI) sequences are enabling clinical study of...

Resilience features capture the stability of an inference method to data decimation. An rs-fMRI dataset (1) is temporally decimated into subsets; each inference method (2) is applied independently to each subset; voxel-wise statistics (3) are estimated; and the parameter maps (4) capture spatial dependencies.

Quantitative Evaluation of Statistical Inference in Resting State Functional MRI

Sep. 30, 2012—Xue Yang and Bennett A. Landman. “Quantitative Evaluation of Statistical Inference in Resting State Functional MRI”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, September 2012 (32% acceptance rate) Full text: https://www.ncbi.nlm.nih.gov/pubmed/23286055 Abstract Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional...

The gamut of observed slices. The labeling objective was to a) delineate the gadolinium enhancing tumor core (T1 images, 1st row) and b) label all abnormally bright tissue (T2 images, 2nd row). The experienced labeler (“truth”, 2nd column) accurately found all structures, while the millers' work spanned a wide range of accuracies.

Collaborative Labeling of Malignant Glioma

May. 4, 2012—Zhoubing Xu, Andrew J. Asman, Eesha Singh, Lola Chambless, Reid Thompson, and Bennett A. Landman, “Collaborative Labeling of Malignant Glioma”, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain Full text: http://ieeexplore.ieee.org/abstract/document/6235763/ Abstract Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection....

Flow chart of steps to create SIMEX Estimated Bias and True Bias maps. Variables refer to terms defined explicitly by equations in the Theory section.

Assessment of bias for MRI diffusion tensor imaging using SIMEX

Sep. 30, 2011—Carolyn B. Lauzon, Andrew Asman, Ciprian Crainiceanu, Brian Caffo, Bennett A. Landman. “Assessment of Bias for MRI Diffusion Tensor Imaging Using SIMEX”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Toronto, Canada, September 2011 (30% acceptance rate) NIHMS ID 301343 Full text: https://www.ncbi.nlm.nih.gov/pubmed/21995019 Abstract Diffusion Tensor Imaging (DTI) is a Magnetic Resonance...

The spatial quality variation exhibited by registered atlases. A representative slice from the true labels (manually drawn) of a target brain is presented in (A). Four example observations of this slice can be seen in (B). The quality of the observations seen in (B) is compared to the true labels seen in (A) to construct the spatial quality variation heat maps presented in (C). Note that the spatial variation is independent of the actual labels of the brain.

Characterizing Spatially Varying Performance to Improve Multi-Atlas Multi-Label Segmentation

Jul. 30, 2011—Andrew J. Asman and Bennett A. Landman. “Characterizing Spatially Varying Performance to Improve Multi-Atlas Multi-Label Segmentation”, In Proceedings of the 2011 International Conference on Information Processing in Medical Imaging (IPMI), Irsee, Bavaria, July 2011 (Oral presentation) (28% acceptance rate) PMC3140117 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140117/ Abstract Segmentation of medical images has become critical to building understanding of biological...