PubMed Mentions
The links below are to publications on PubMed referring to IBSR. This list is gathered weekly from PubMed automatically.
| Publication/References | |
| Automatic volumetry on MR brain images can support diagnostic decision making. Description: Heckemann, Rolf A, et al. Automatic volumetry on MR brain images can support diagnostic decision making. ''BMC Med Imaging''. 2008 May 23; '''8''': 9 | |
| Joint brain parametric T1-map segmentation and RF inhomogeneity calibration. Description: Chen, Ping-Feng, et al. Joint brain parametric T1-map segmentation and RF inhomogeneity calibration. ''Int J Biomed Imaging''. 2009; '''2009''': 269525 | |
| Online resource for validation of brain segmentation methods. Description: Shattuck, David W, et al. Online resource for validation of brain segmentation methods. ''Neuroimage''. 2009 Apr 1; '''45''' (2):431-9 | |
| Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Description: Chupin, M, et al. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. ''Neuroimage''. 2009 Jul 1; '''46''' (3):749-61 | |
| Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Description: Ashburner, John, et al. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. ''Neuroimage''. 2011 Apr 1; '''55''' (3):954-67 | |
| Spatial based expectation maximizing (EM). Description: Balafar, M A. Spatial based expectation maximizing (EM). ''Diagn Pathol''. 2011 Oct 26; '''6''': 103 | |
| Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. Description: Eggert, Lucas D, et al. Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. ''PLoS One''. 2012; '''7''' (9):e45081 | |
| Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. Description: Narayana, Ponnada A, et al. Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. ''Neuroimage Clin''. 2012; '''2''': 120-31 | |
| Region-based Active Contour Model based on Markov Random Field to Segment Images with Intensity Non-Uniformity and Noise. Description: Shahvaran, Zahra, et al. Region-based Active Contour Model based on Markov Random Field to Segment Images with Intensity Non-Uniformity and Noise. ''J Med Signals Sens''. 2012 Jan; '''2''' (1):17-24 | |
| Nonrigid medical image registration based on mesh deformation constraints. Description: Lin, XiangBo, et al. Nonrigid medical image registration based on mesh deformation constraints. ''Comput Math Methods Med''. 2013; '''2013''': 373082 | |
| Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI. Description: El Harchaoui, Nour-Eddine, et al. Unsupervised approach data analysis based on fuzzy possibilistic clustering: application to medical image MRI. ''Comput Intell Neurosci''. 2013; '''2013''': 435497 | |
| A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine. Description: Kasiri, Keyvan, et al. A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine. ''J Med Signals Sens''. 2013 Oct; '''3''' (4):232-43 | |
| An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network. Description: Amiri, S, et al. An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network. ''J Biomed Phys Eng''. 2013 Dec; '''3''' (4):115-22 | |
| Automated hippocampal segmentation in patients with epilepsy: available free online. Description: Winston, Gavin P, et al. Automated hippocampal segmentation in patients with epilepsy: available free online. ''Epilepsia''. 2013 Dec; '''54''' (12):2166-73 | |
| Effect of intrinsic and extrinsic factors on global and regional cortical thickness. Description: Govindarajan, Koushik A, et al. Effect of intrinsic and extrinsic factors on global and regional cortical thickness. ''PLoS One''. 2014; '''9''' (5):e96429 | |
| Magnetic resonance image tissue classification using an automatic method. Description: Yazdani, Sepideh, et al. Magnetic resonance image tissue classification using an automatic method. ''Diagn Pathol''. 2014 Dec 24; '''9''': 207 | |
| Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age. Description: Fillmore, Paul T, et al. Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age. ''Front Aging Neurosci''. 2015; '''7''': 44 | |
| A Unified Framework for Brain Segmentation in MR Images. Description: Yazdani, S, et al. A Unified Framework for Brain Segmentation in MR Images. ''Comput Math Methods Med''. 2015; '''2015''': 829893 | |
| MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans. Description: Mendrik, Adrienne M, et al. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans. ''Comput Intell Neurosci''. 2015; '''2015''': 813696 | |
| Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation. Description: Maji, Pradipta, et al. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation. ''PLoS One''. 2015; '''10''' (4):e0123677 | |
| SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests. Description: Serag, Ahmed, et al. SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests. ''Front Neuroinform''. 2017; '''11''': 2 | |
| Automatic brain tissue segmentation based on graph filter. Description: Kong, Youyong, et al. Automatic brain tissue segmentation based on graph filter. ''BMC Med Imaging''. 2018 May 9; '''18''' (1):9 | |
| Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks. Description: Novosad, Philip, et al. Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks. ''Hum Brain Mapp''. 2020 Feb 1; '''41''' (2):309-327 |