Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 174 entries
Sorted by: Best Match Show Resources per page
Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging.

Frontiers in aging neuroscience

Liu S, Cai W, Pujol S, Kikinis R, Feng DD.
PMID: 26941639
Front Aging Neurosci. 2016 Feb 23;8:23. doi: 10.3389/fnagi.2016.00023. eCollection 2016.

The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy...

Cite
Liu S, Cai W, Pujol S, et al. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci. 2016;8:23doi: 10.3389/fnagi.2016.00023.
Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D. D. (2016). Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Frontiers in aging neuroscience, 823. https://doi.org/10.3389/fnagi.2016.00023
Liu, Sidong, et al. "Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging." Frontiers in aging neuroscience vol. 8 (2016): 23. doi: https://doi.org/10.3389/fnagi.2016.00023
Liu S, Cai W, Pujol S, Kikinis R, Feng DD. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci. 2016 Feb 23;8:23. doi: 10.3389/fnagi.2016.00023. eCollection 2016. PMID: 26941639; PMCID: PMC4763344.
Copy Download .nbib Format: NLM
Convex Clustering with Exemplar-Based Models.

Advances in neural information processing systems

Lashkari D, Golland P.
PMID: 26140013
Adv Neural Inf Process Syst. 2007;20.

Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization problem is inherently a gradient-descent method and is sensitive to initialization. The...

Cite
Lashkari D, Golland P. Convex Clustering with Exemplar-Based Models. Adv Neural Inf Process Syst. 2007;20
Lashkari, D., & Golland, P. (2007). Convex Clustering with Exemplar-Based Models. Advances in neural information processing systems, 20.
Lashkari, Danial, and Golland, Polina. "Convex Clustering with Exemplar-Based Models." Advances in neural information processing systems vol. 20 (2007).
Lashkari D, Golland P. Convex Clustering with Exemplar-Based Models. Adv Neural Inf Process Syst. 2007;20. PMID: 26140013; PMCID: PMC4485428.
Copy Download .nbib Format: NLM
BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.

The annals of applied statistics

Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG.
PMID: 26900412
Ann Appl Stat. 2015 Dec;9(4):2153-2178. doi: 10.1214/15-AOAS879.

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346...

Cite
Lee E, Zhu H, Kong D, et al. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. Ann Appl Stat. 2015;9(4):2153-2178doi: 10.1214/15-AOAS879.
Lee, E., Zhu, H., Kong, D., Wang, Y., Giovanello, K. S., & Ibrahim, J. G. (2015). BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. The annals of applied statistics, 9(4), 2153-2178. https://doi.org/10.1214/15-AOAS879
Lee, Eunjee, et al. "BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE." The annals of applied statistics vol. 9,4 (2015): 2153-2178. doi: https://doi.org/10.1214/15-AOAS879
Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. Ann Appl Stat. 2015 Dec;9(4):2153-2178. doi: 10.1214/15-AOAS879. PMID: 26900412; PMCID: PMC4756762.
Copy Download .nbib Format: NLM
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations.

Advances in neural information processing systems

Lashkari D, Sridharan R, Golland P.
PMID: 24839377
Adv Neural Inf Process Syst. 2010 Jan 01;23:1252-1260.

We present a model that describes the structure in the responses of different brain areas to a set of stimuli in terms of

Cite
Lashkari D, Sridharan R, Golland P. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. Adv Neural Inf Process Syst. 2010;23:1252-1260
Lashkari, D., Sridharan, R., & Golland, P. (2010). Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. Advances in neural information processing systems, 231252-1260.
Lashkari, Danial, et al. "Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations." Advances in neural information processing systems vol. 23 (2010): 1252-1260.
Lashkari D, Sridharan R, Golland P. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. Adv Neural Inf Process Syst. 2010 Jan 01;23:1252-1260. PMID: 24839377; PMCID: PMC4022600.
Copy Download .nbib Format: NLM
Intrinsic Regression Models for Medial Representation of Subcortical Structures.

Journal of the American Statistical Association

Shi X, Zhu H, Ibrahim JG, Liang F, Lieberman J, Styner M.
PMID: 23794769
J Am Stat Assoc. 2012 Mar 01;107(497):12-23. doi: 10.1080/01621459.2011.643710.

The aim of this paper is to develop a semiparametric model for describing the variability of the medial representation of subcortical structures, which belongs to a Riemannian manifold, and establishing its association with covariates of interest, such as diagnostic...

Cite
Shi X, Zhu H, Ibrahim JG, et al. Intrinsic Regression Models for Medial Representation of Subcortical Structures. J Am Stat Assoc. 2012;107(497):12-23doi: 10.1080/01621459.2011.643710.
Shi, X., Zhu, H., Ibrahim, J. G., Liang, F., Lieberman, J., & Styner, M. (2012). Intrinsic Regression Models for Medial Representation of Subcortical Structures. Journal of the American Statistical Association, 107(497), 12-23. https://doi.org/10.1080/01621459.2011.643710
Shi, Xiaoyan, et al. "Intrinsic Regression Models for Medial Representation of Subcortical Structures." Journal of the American Statistical Association vol. 107,497 (2012): 12-23. doi: https://doi.org/10.1080/01621459.2011.643710
Shi X, Zhu H, Ibrahim JG, Liang F, Lieberman J, Styner M. Intrinsic Regression Models for Medial Representation of Subcortical Structures. J Am Stat Assoc. 2012 Mar 01;107(497):12-23. doi: 10.1080/01621459.2011.643710. PMID: 23794769; PMCID: PMC3685886.
Copy Download .nbib Format: NLM
Functional Geometry Alignment and Localization of Brain Areas.

Advances in neural information processing systems

Langs G, Golland P, Tie Y, Rigolo L, Golby AJ.
PMID: 24808719
Adv Neural Inf Process Syst. 2010;1:1225-1233.

Matching functional brain regions across individuals is a challenging task, largely due to the variability in their location and extent. It is particularly difficult, but highly relevant, for patients with pathologies such as brain tumors, which can cause substantial...

Cite
Langs G, Golland P, Tie Y, et al. Functional Geometry Alignment and Localization of Brain Areas. Adv Neural Inf Process Syst. 2010;1:1225-1233
Langs, G., Golland, P., Tie, Y., Rigolo, L., & Golby, A. J. (2010). Functional Geometry Alignment and Localization of Brain Areas. Advances in neural information processing systems, 11225-1233.
Langs, Georg, et al. "Functional Geometry Alignment and Localization of Brain Areas." Advances in neural information processing systems vol. 1 (2010): 1225-1233.
Langs G, Golland P, Tie Y, Rigolo L, Golby AJ. Functional Geometry Alignment and Localization of Brain Areas. Adv Neural Inf Process Syst. 2010;1:1225-1233. PMID: 24808719; PMCID: PMC4010233.
Copy Download .nbib Format: NLM
Dynamic Active Contours for Visual Tracking.

IEEE transactions on automatic control

Niethammer M, Tannenbaum A, Angenent S.
PMID: 23729836
IEEE Trans Automat Contr. 2006;51(4):562-579. doi: 10.1109/TAC.2006.872837.

Visual tracking using active contours is usually set in a static framework. The active contour tracks the object of interest in a given frame of an image sequence. A subsequent prediction step ensures good initial placement for the next...

Cite
Niethammer M, Tannenbaum A, Angenent S. Dynamic Active Contours for Visual Tracking. IEEE Trans Automat Contr. 2006;51(4):562-579doi: 10.1109/TAC.2006.872837.
Niethammer, M., Tannenbaum, A., & Angenent, S. (2006). Dynamic Active Contours for Visual Tracking. IEEE transactions on automatic control, 51(4), 562-579. https://doi.org/10.1109/TAC.2006.872837
Niethammer, Marc, et al. "Dynamic Active Contours for Visual Tracking." IEEE transactions on automatic control vol. 51,4 (2006): 562-579. doi: https://doi.org/10.1109/TAC.2006.872837
Niethammer M, Tannenbaum A, Angenent S. Dynamic Active Contours for Visual Tracking. IEEE Trans Automat Contr. 2006;51(4):562-579. doi: 10.1109/TAC.2006.872837. PMID: 23729836; PMCID: PMC3666594.
Copy Download .nbib Format: NLM
EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)

Venkataraman A, Van Dijk KR, Buckner RL, Golland P.
PMID: 26028993
Proc IEEE Int Conf Acoust Speech Signal Process. 2009 Apr;2009:441-444. doi: 10.1109/ICASSP.2009.4959615.

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering...

Cite
Venkataraman A, Van Dijk KR, Buckner RL, et al. EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING. Proc IEEE Int Conf Acoust Speech Signal Process. 2009;2009:441-444doi: 10.1109/ICASSP.2009.4959615.
Venkataraman, A., Van Dijk, K. R., Buckner, R. L., & Golland, P. (2009). EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2009441-444. https://doi.org/10.1109/ICASSP.2009.4959615
Venkataraman, Archana, et al. "EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING." Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) vol. 2009 (2009): 441-444. doi: https://doi.org/10.1109/ICASSP.2009.4959615
Venkataraman A, Van Dijk KR, Buckner RL, Golland P. EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING. Proc IEEE Int Conf Acoust Speech Signal Process. 2009 Apr;2009:441-444. doi: 10.1109/ICASSP.2009.4959615. PMID: 26028993; PMCID: PMC4449336.
Copy Download .nbib Format: NLM
Affine Registration of label maps in Label Space.

Journal of computing

Rathi Y, Malcolm J, Bouix S, Tannenbaum A, Shenton ME.
PMID: 26029473
J Comput. 2010 Apr;2(4):1-11.

Two key aspects of coupled multi-object shape analysis and atlas generation are the choice of representation and subsequent registration methods used to align the sample set. For example, a typical brain image can be labeled into three structures: grey...

Cite
Rathi Y, Malcolm J, Bouix S, et al. Affine Registration of label maps in Label Space. J Comput. 2010;2(4):1-11
Rathi, Y., Malcolm, J., Bouix, S., Tannenbaum, A., & Shenton, M. E. (2010). Affine Registration of label maps in Label Space. Journal of computing, 2(4), 1-11.
Rathi, Yogesh, et al. "Affine Registration of label maps in Label Space." Journal of computing vol. 2,4 (2010): 1-11.
Rathi Y, Malcolm J, Bouix S, Tannenbaum A, Shenton ME. Affine Registration of label maps in Label Space. J Comput. 2010 Apr;2(4):1-11. PMID: 26029473; PMCID: PMC4447523.
Copy Download .nbib Format: NLM
Automatic Sulcal Curve Extraction on the Human Cortical Surface.

Proceedings of SPIE--the International Society for Optical Engineering

Lyu I, Kim SH, Styner M.
PMID: 26028801
Proc SPIE Int Soc Opt Eng. 2015;9413. doi: 10.1117/12.2078291.

The recognition of sulcal regions on the cortical surface is an important task to shape analysis and landmark detection. However, it is challenging especially in a complex, rough human cortex. In this paper, we focus on the extraction of...

Cite
Lyu I, Kim SH, Styner M. Automatic Sulcal Curve Extraction on the Human Cortical Surface. Proc SPIE Int Soc Opt Eng. 2015;9413doi: 10.1117/12.2078291.
Lyu, I., Kim, S. H., & Styner, M. (2015). Automatic Sulcal Curve Extraction on the Human Cortical Surface. Proceedings of SPIE--the International Society for Optical Engineering, 9413. https://doi.org/10.1117/12.2078291
Lyu, Ilwoo, et al. "Automatic Sulcal Curve Extraction on the Human Cortical Surface." Proceedings of SPIE--the International Society for Optical Engineering vol. 9413 (2015). doi: https://doi.org/10.1117/12.2078291
Lyu I, Kim SH, Styner M. Automatic Sulcal Curve Extraction on the Human Cortical Surface. Proc SPIE Int Soc Opt Eng. 2015;9413. doi: 10.1117/12.2078291. PMID: 26028801; PMCID: PMC4449147.
Copy Download .nbib Format: NLM
Interactive Whole-Heart Segmentation in Congenital Heart Disease.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P.
PMID: 26889498
Med Image Comput Comput Assist Interv. 2015 Oct;9351:80-88. doi: 10.1007/978-3-319-24574-4_10. Epub 2015 Nov 18.

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical...

Cite
Pace DF, Dalca AV, Geva T, et al. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015;9351:80-88doi: 10.1007/978-3-319-24574-4_10.
Pace, D. F., Dalca, A. V., Geva, T., Powell, A. J., Moghari, M. H., & Golland, P. (2015). Interactive Whole-Heart Segmentation in Congenital Heart Disease. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 935180-88. https://doi.org/10.1007/978-3-319-24574-4_10
Pace, Danielle F, et al. "Interactive Whole-Heart Segmentation in Congenital Heart Disease." Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention vol. 9351 (2015): 80-88. doi: https://doi.org/10.1007/978-3-319-24574-4_10
Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015 Oct;9351:80-88. doi: 10.1007/978-3-319-24574-4_10. Epub 2015 Nov 18. PMID: 26889498; PMCID: PMC4753059.
Copy Download .nbib Format: NLM
Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation.

Statistical atlases and computational models of the heart. STACOM (Workshop)

Depa M, Sabuncu MR, Holmvang G, Nezafat R, Schmidt EJ, Golland P.
PMID: 26090522
Stat Atlases Comput Models Heart. 2010;6364:85-94. doi: 10.1007/978-3-642-15835-3_9.

Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate...

Cite
Depa M, Sabuncu MR, Holmvang G, et al. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. Stat Atlases Comput Models Heart. 2010;6364:85-94doi: 10.1007/978-3-642-15835-3_9.
Depa, M., Sabuncu, M. R., Holmvang, G., Nezafat, R., Schmidt, E. J., & Golland, P. (2010). Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. Statistical atlases and computational models of the heart. STACOM (Workshop), 636485-94. https://doi.org/10.1007/978-3-642-15835-3_9
Depa, Michal, et al. "Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation." Statistical atlases and computational models of the heart. STACOM (Workshop) vol. 6364 (2010): 85-94. doi: https://doi.org/10.1007/978-3-642-15835-3_9
Depa M, Sabuncu MR, Holmvang G, Nezafat R, Schmidt EJ, Golland P. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. Stat Atlases Comput Models Heart. 2010;6364:85-94. doi: 10.1007/978-3-642-15835-3_9. PMID: 26090522; PMCID: PMC4469076.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 174 entries