Probabilistic segmentation of white matter lesions in MR imaging

By P. Anbeek, K. Vincken, M. Osch, R. Bisschops, J. Grond. NeuroImage, 2004

376 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/5222b27ca66cf17799bd7392a5cfd6397dec0871

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sébastien Ourselin
2. Daniel S. Reich
3. Peter A. Calabresi
4. Navid Shiee
5. Pierre-Louis Bazin
6. Arzu Ozturk
7. M. Jorge Cardoso
8. Claudia A. M. Gandini Wheeler-Kingshott

In Neurology

1. Sébastien Ourselin
2. Monique M. B. Breteler
3. M. Ikram
4. Mark Jenkinson
5. Wiro J. Niessen
6. Mark Mühlau
7. Henri A. Vrooman
8. Meike W. Vernooij

Citations in the corpus

• A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions (2009, positive): “…T1 image, and segmented the lesions on the T2 image with masked CSF. Similar methods have been proposed for the detection of white matter signal abnormalities that frequently occur in Alzheimer's disease and older populations (Anbeek et al., 2004; Admiraal-Behloul et al., 2005; Lao et al., 2008).”
• Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017, positive): “Examples of this include: k -nearest neighbors ( k - NN) (Anbeek et al., 2004), a hierarchical Hidden Markov random field (Sajja et al., 2004, 2006); an unsupervised Bayesian lesion classifier with various regions of the brain having different intensity distributions (Harmouche et al., 2006); a…”
• An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis (2012, methodology): “…et al., 2001 ) EM alg. with MRF and OD T1, T2, PD Manual segmentation 23 / (Ferrari et al., 2003) SVM T1, T2, FLAIR Manual segmentation 18 / (Anbeek et al., 2004) KNN T1, T2, PD, FLAIR Manual segmentation 19 / (Ait-Ali et al., 2005) Robust EM, trimmed likelihood estimator and OD T1, T2, PD…”
• BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities (2016, methodology): “Regarding the number of voxels included in the training dataset, in the algorithm proposed by Anbeek and colleagues (Anbeek et al., 2004) a fi xed number (20”
• Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification (2007, methodology): “Ashburner and Friston (1997) used probability maps to fit a Gaussian mixture module for the different tissue types present in the image data.”