Author

Machine
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learning approximat
ion of an efficacy map.
Patient MRI image overlaid with probability of positive response
assessed on a sliding patch basis (assuming an infeasible stimulation at every voxel). Green crosses indicate actual intraope
rative
stimulation locations with a positi
ve response. Blue circles indicate intraoperative stimulation locations with a null response.

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps

Dec. 10, 2018—Bermudez, C., Rodriguez, W., Huo, Y., Hainline, A. E., Li, R., Shults, R., … & Landman, B. A. (2018). Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps. arXiv preprint arXiv:1811.10415. Full Text: https://arxiv.org/abs/1811.10415 Abstract Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a...

Figure 2
The pipeline for iterative definition of one bundle – the left cingulum in native space of one monkey.

A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging.

Feb. 27, 2016—Yurui Gao, Prasanna Parvathaneni, Kurt G. Schilling, Zhoubing Xu, Ann S. Choe, Bennett A. Landman, Adam M. Anderson. A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text Abstract...

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services

Feb. 27, 2016—Shunxing Bao, Stephen M. Damon, Bennett A. Landman, Aniruddha Gokhale. “Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Performance+Management+of+High+Performance+Computing+for+Medical+Image+Processing+in+Amazon+Web+Services Abstract Adopting high performance cloud computing for medical image processing is a popular...

Improving Cerebellar Segmentation with Statistical Fusion

Feb. 27, 2016—Andrew J. Plassard, Zhen Yang, Swati D. Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman. “Improving Cerebellar Segmentation with Statistical Fusion. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Improving+Cerebellar+Segmentation+with+Statistical+Fusion Abstract The cerebellum is a somatotopically organized central component of the central nervous system well known...

Figure 3
Qualitative results for the optimal multi-atlas segmentation approach for 7 subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2D slice. The bottom rows show point-wise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight over segmentations of the ONs can be observed in certain cases (subjects 4 and 7).

Robust Optic Nerve Segmentation on Clinically Acquired CT

Feb. 1, 2014—Swetasudha Panda, Andrew J. Asman, Michael P. DeLis, Louise A. Mawn, Robert L. Galloway, Bennett A. Landman. “Robust Optic Nerve Segmentation on Clinically Acquired CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817810 Abstract The optic nerve is a sensitive central nervous system structure,...

Figure 4
Rendering of the manually labeled cortical regions ().

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

Feb. 1, 2014—Yurui Gao, Shweta P. Khare, Swetasudha Panda, Ann S Choe, Iwona Stepniewska, Xia Li, Zhoahua Ding, Adam Anderson, Bennett A. Landman, “A brain MRI atlas of the common squirrel monkey, Saimiri sciureus.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817811 Abstract The common squirrel...

Figure 3
Flowchart of the proposed method. The atlas labels are co-registered to construct a pose-free implicit parametric shape model, including the mean and the modes of variation of the spleen shape. The atlas images are registered to the target image, based on which the atlas labels are propagated to the target space. The locally weighted vote yields the initial fusion result of the registered atlas labels weighted by the local intensity similarity between the registered atlas images and the target image in the form of a fuzzy estimate of label probability and a binary estimate of spleen segmentation. The binary image of the mean shape from the pre-constructed spleen shape model is registered to that of the current estimate with two distinct effective ranges, i.e., (1) the whole volume of both image and (2) the mean shape region, of the similarity metric of registration so that the pose-free shape model is transformed into the target space. The current estimate of the spleen is then projected to the two registered shape models. The shape projections are converted into probabilistic priors to adjust the label probability from locally weighted vote, and then generate a new estimate of the spleen. The estimate can be refined with iterative adjustment.

Shape-Constrained Multi-Atlas Segmentation of Spleen in CT

Feb. 1, 2014—Z. Xu, B. Li, S. Panda, A. Asman, Kristen. L. Merkle, Peter L. Shanahan, Richard G. Abramson, B. Landman. “Shape-Constrained Multi-Atlas Segmentation of Spleen in CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Shape-Constrained+Multi-Atlas+Segmentation+of+Spleen+in+CT Abstract Spleen segmentation on clinically acquired CT data is a challenging problem given...

On study design in neuroimaging heritability analyses.

Feb. 1, 2014—Mary Ellen Koran, Neda Jahanshad, Bo Li, Tricia A. Thornton-Wells, David C. Glahn, Paul M. Thompson, John Blangero, Thomas E. Nichols, Peter Kochunov, Bennett A. Landman. “On study design in neuroimaging heritability analyses.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.researchgate.net/publication/269322665_On_Study_Design_in_Neuroimaging_Heritability_Analyses Abstract Imaging genetics is an emerging...

Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.

Feb. 1, 2014—Datteri, R.D., Asman, A.J., Landman, B.A., Dawant, B.M., “Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.” Proc. SPIE, Medical Imaging 2014: Image Processing, February 2014. † Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1852464 Abstract Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a...

Figure 2
Illustration of minimally rater performance relative to an experienced rater for T1 data (A) and T2 data (B). The top row shows 200 slices which have been expertly labeled sorted by the median overlap with minimally trained raters. The center row shows the number of times each slice has been labeled. The final row illustrates an arbitrary example from the lowest, median, and highest accuracy slices.

Collaborative Labeling of Malignant Glioma with WebMILL: A First Look

Feb. 1, 2012—E. Singh, Z. Xu, A. Asman, L. Chambless, R. Thompson and B. Landman. “Collaborative Labeling of Malignant Glioma with WebMILL: A First Look.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Collaborative+Labeling+of+Malignant+Glioma+with+WebMILL%3A+A+First+Look Abstract Malignant gliomas are the most common form of primary neoplasm in the central nervous...

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