Image Processing Category

Constructing Statistically Unbiased Cortical Surface Templates Using Feature Space Covariance

Dec. 19, 2017—Citation: ” Constructing statistically unbiased cortical surface templates using feature-space covariance”. Prasanna Parvathaneni, Ilwoo Lyu, Justin A. Blaber, Yuankai Huo, Allison E. Hainline, Neil D. Woodward, Hakmook Kang, Bennett A. Landman In SPIE Medical Imaging, International Society for Optics and Photonics, 2018 (Accepted). Abstract The choice of surface template plays an important role in cross-sectional subject analyses...

Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Aug. 28, 2017—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...

Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Jun. 1, 2017—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)...

Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features

May. 15, 2017—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...

This figure shows the failures for seven problematic volumes. Both whole brain segmentations and cortical surfaces on axial slices are provided. The areas in red rectangles show the global failures while the areas in blue rectangles show the local failures. MaCRUISE did not exhibit such failures in any images.

Combining Multi-atlas Segmentation with Brain Surface Estimation

Mar. 1, 2016—Yuankai Huo, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, Bennett A. Landman.2016 “Combining Multi-atlas Segmentation with Brain Surface Estimation,” In Proceedings of the SPIE Medical Imaging Conference 2016. Oral presentation. Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2506157 Pubmed Abstract Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential...

$Example potential application of HUBRIS for the reconstruction of improved diffusion tensor imaging (DTI) fractional anisotropy (FA) maps using a multi-shot acquisition. The presented color maps from single-shot (a) and multi-shot (b) echo-planar imaging (EPI) data show multi-shot EPI exhibits reduced blurring and geometric distortions. The generation of the improved images requires a custom reconstruction algorithm that can be accessed by HUBRIS.$

“Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System”

Aug. 11, 2011—Kelsie Covington, E. Brian Welch, Bennett A. Landman. “Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) PMC3154704† Full text Abstract Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities...

mpact of outliers in BPM. (A) is one of the regressor images without outliers. The main modality images are: Y = 3X inside a spherical region, Y = constant outside the region. (B) is the Beta estimation when there are no outliers. (C) is the significant results when there are no outliers, (D) is the shifted regressor image, the center cube shifts to another region. The center cube of the paired main modality image shifts to the same region. (E) is the estimated Beta from original BPM when there are outliers. (F) is the significant results from original BPM when there are outliers.

“Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”

Feb. 1, 2011—Xue Yang, Lori Beason-Held, Susan M. Resnick, Bennett A. Landman. “Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3103184 † Full text Abstract Mapping the quantitative relationship between structure and function in the human brain is an...