| Year |
Citation |
Score |
| 2021 |
Pietrosanu M, Zhang L, Seres P, Elkady A, Wilman AH, Kong L, Cobzas D. Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain. Frontiers in Human Neuroscience. 15: 641616. PMID 33708081 DOI: 10.3389/fnhum.2021.641616 |
0.313 |
|
| 2019 |
Elkady AM, Cobzas D, Sun H, Seres P, Blevins G, Wilman AH. Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls. Multiple Sclerosis and Related Disorders. 33: 107-115. PMID 31181540 DOI: 10.1016/J.Msard.2019.05.028 |
0.34 |
|
| 2018 |
Elkady AM, Cobzas D, Sun H, Blevins G, Wilman AH. Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects. Journal of Magnetic Resonance Imaging : Jmri. PMID 29537720 DOI: 10.1002/Jmri.26004 |
0.337 |
|
| 2017 |
Zhang L, Cobzas D, Wilman AH, Kong L. Significant Anatomy Detection through Sparse Classification: A Comparative Study. Ieee Transactions On Medical Imaging. PMID 28783628 DOI: 10.1109/Tmi.2017.2735239 |
0.336 |
|
| 2017 |
Elkady AM, Cobzas D, Sun H, Blevins G, Wilman AH. Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter. Journal of Magnetic Resonance Imaging : Jmri. PMID 28301067 DOI: 10.1002/Jmri.25682 |
0.367 |
|
| 2017 |
Fujiwara E, Kmech JA, Cobzas D, Sun H, Seres P, Blevins G, Wilman AH. Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis. Ajnr. American Journal of Neuroradiology. PMID 28232497 DOI: 10.3174/Ajnr.A5109 |
0.336 |
|
| 2016 |
Popuri K, Cobzas D, Esfandiari N, Baracos V, Jagersand M. Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle. Ieee Transactions On Medical Imaging. 35: 512-20. PMID 26415164 DOI: 10.1109/Tmi.2015.2479252 |
0.329 |
|
| 2015 |
Cobzas D, Sun H, Walsh AJ, Lebel RM, Blevins G, Wilman AH. Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. Journal of Magnetic Resonance Imaging : Jmri. PMID 25980643 DOI: 10.1002/Jmri.24951 |
0.352 |
|
| 2014 |
Rudyanto RD, Kerkstra S, van Rikxoort EM, Fetita C, Brillet PY, Lefevre C, Xue W, Zhu X, Liang J, Öksüz I, Ünay D, Kadipa?ao?lu K, Estépar RS, Ross JC, Washko GR, ... ... Cobzas D, et al. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18: 1217-32. PMID 25113321 DOI: 10.1016/J.Media.2014.07.003 |
0.328 |
|
| 2009 |
Chung H, Cobzas D, Birdsell L, Lieffers J, Baracos V. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis Proceedings of Spie. 7261. DOI: 10.1117/12.812412 |
0.344 |
|
| 2009 |
Popuri K, Cobzas D, Jagersand M, Shah SL, Murtha A. 3D variational brain tumor segmentation on a clustered feature set Progress in Biomedical Optics and Imaging - Proceedings of Spie. 7259. DOI: 10.1117/12.811029 |
0.338 |
|
| 2009 |
Cobzas D, Jagersand M, Sturm P. 3D SSD tracking with estimated 3D planes Image and Vision Computing. 27: 69-79. DOI: 10.1016/J.Imavis.2006.10.008 |
0.325 |
|
| 2006 |
Cobzas D, Jagersand M. 3D SSD tracking from uncalibrated video Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3667: 25-37. DOI: 10.1007/11676959_3 |
0.329 |
|
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