Medical-image Analysis and Statistical Interpretation (MASI) Lab

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...

Figure 1 shows the results of ST analysis in a region with in-plane fiber crossings. We find that, in both MRI and histology, the percent of voxels with crossing fibers actually increases as we increase the resolution. The
middle and bottom rows highlight “single” fiber regions in red and “crossing” fibers as green, and qualitatively
show that many regions with a “single” fiber population at a coarse resolution exhibit crossing patterns when
processed at a finer resolution.

Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?

Sep. 1, 2016—Kurt G. Schilling, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, Adam W Anderson. “Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?” In Proceedings of the ISMRM Workshop on Diffusion MRI. Lisbon, Portugal, September 2016. Oral presentation. Full text: PDF Abstract It is now widely recognized that voxels with crossing...

Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo

Aug. 31, 2016—Kurt Schilling, Yurui Gao, Iwona Stepniewska, Ann S. Choe, Bennett A. Landman, and Adam W Anderson. “Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo”. Magnetic Resonance Imaging. In press August 2015 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27587226 Abstract Purpose: Animal models are needed to better understand the relationship between diffusion...

$(A) Significant associations with plasma C22:0 and subsequent FA. (A) Red–yellow indicates higher plasma C22:0 associated with lower FA in white matter regions, while cyan–blue indicates higher plasma C22:0 associated with higher FA. Regional statistics are overlaid onto an FA image from a single subject; results are not corrected for multiple comparisons. (B) ACR FA by mean centered Cer 22:0. (B) Association between FA in ACR and Cer 22:0. The residual FA of ACR was calculated by regressing standardized FA on baseline age, sex, interval between blood draw and DTI, diabetes, BMI, scanner, and ratio of white matter lesions to intracranial volume. This is shown plotted against mean centered Cer 22:0 with a linear fit overlaid. Abbreviations: ACR, anterior corona radiata; ALIC, anterior limb of internal capsule; BMI, body mass index; CG, cingulum of cingulate gyrus; DTI, diffusion tensor imaging; FA, fractional anisotropy; Scc, splenium of corpus callosum; SLF, superior longitudinal fasciculus; SS, sagittal stratum. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)$

Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.

Jul. 31, 2016—Christopher E. Gonzalez, Vijay K. Venkatraman, Yang An, Bennett A. Landman, Christos Davatzikos, Veera Venkata Ratnam Bandaru, Norman J. Haughey, Luigi Ferruci, Michelle M. Mielke, Susan M. Resnick. “Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.” Neurobiology of Aging. July 2016. Volume 43, Pages 156–163 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/27255825 Abstract Sphingolipids...

(A) A standard fully connected neural network where each layer's node is connected to each node from the
previous layer.7 (B) A convolutional neural network connecting a covolutional layer to a pooling layer.

Deep Learning for Brain Tumor Classification

Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...

Investigation of Bias in Continuous Medical Image Label Fusion

Jun. 30, 2016—Fangxu Xing; Jerry Prince; Bennett Landman. Investigation of Bias in Continuous Medical Image Label Fusion. PLoS One. 2016 Jun 3;11(6):e0155862. PMC4892597 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27258158 Abstract Image labeling is essential for analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms, both of which suffer from errors....

Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT

Jun. 30, 2016—Zhoubing Xu, Christopher P. Lee, Marc Modat, Daniel Rueckert, Sebastien Ourselin, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT”. IEEE Transactions of Biomedical Engineering. 2016 Aug;63(8):1563-72. PMC4972188 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27254856 Abstract Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal...

Consistent Cortical Reconstruction and Multi-atlas Brain Segmentation

Apr. 30, 2016—Yuankai Huo, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, Bennett A. Landman. “Consistent Cortical Reconstruction and Multi-atlas Brain Segmentation”. NeuroImage. Volume 138, September 2016, Pages 197–210 PMC4927397 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27184203 Abstract Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can...

Qualitative comparisons between body-wise and organ-wise approaches on spleen (red), right kidney (yellow), and left kidney (blue). Padded bounding boxes in correspondent colors indicate the ROIs for organ-wise registrations.

Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs

Apr. 15, 2016—Zhoubing Xu, Sahil A. Panjwani, Christopher P. Lee, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, “Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845963/ Abstract Identifying cross-sectional and...

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