Author

Figure 3
Kurtosis of weighted residuals. The Kurtosis maps of the residuals from the weighted data are shown in the first column. The middle and the right column display one voxel residuals across the first 100 scans inside the cerebrospinal fluid region and the white matter region respectively.

A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T

Feb. 1, 2012—Xue Yang, Martha J. Holmes, Allen T. Newton, Victoria L. Morgan, Bennett A. Landman. “A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) NIHMS341654† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=A+Comparison+of+Distributional+Considerations+with+Statistical+Analysis+of+Resting+State+fMRI+at+3T+and+7T Abstract Ultra-high field 7T magnetic resonance...

A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation

Feb. 1, 2012—Yuan Liu, Benoit M. Dawant, Srivatsan Pallavaram, Joseph S. Neimat, Pierre-François D’Haese, Ryan D. Datteri, Bennett A. Landman and Jack H. Noble. “A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) † Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1285627 Abstract In deep brain...

Figure 2
Results from the controlled rater observation simulation. The target intensity image and the corresponding true labels can be seen (A) and (B), respectively. The set of input observations used in this experiment can be seen in (C). The results of the four algorithms, Majority Vote, STAPLE, Binary-COLLATE and Multi-COLLATE can be seen in (D)–(G). Note that the 3 consensus-level implementation of Multi-COLLATE is the only algorithm that gets the correct answer. The 3 energy in each of the consensus levels can be seen in (H)–(J). Note that by distributing the non-consensus voxels into two separate levels, (H) and (I), the estimation process converges to the correct answer.

Generalized Statistical Label Fusion using Multiple Consensus Levels

Feb. 1, 2012—Z. Xu, A. Asman and B. Landman. “Generalized Statistical Label Fusion using Multiple Consensus Levels.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) PMC3438516† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=%E2%80%9CGeneralized+Statistical+Label+Fusion+using+Multiple+Consensus+Levels. Abstract Segmentation plays a critical role in exposing connections between biological structure and function. The process of label fusion collects and...

Simultaneous Segmentation and Statistical Label Fusion.

Feb. 1, 2012—A. Asman and B. Landman. “Simultaneous Segmentation and Statistical Label Fusion.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24357909 Abstract Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common...