A unifying framework for partial volume segmentation of brain MR images

By K. V. Leemput, Frederik Maes, Dirk Vandermeulen, Paul Suetens. IEEE Transactions on Medical Imaging, 2003

337 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7da777b5eaaec3ea19f3dc9d359d4dde05848597

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data (2011, background): “Related technological developments model partial volume effects for increased accuracy in brain segmentation (Ruan et al. 2000; Ballester et al. 2002; Leemput et al. 2003).”
Fast and robust parameter estimation for statistical partial volume models in brain MRI (2004, methodology): “An elegant solution to this problem based on a Monte Carlo EM-algorithm (Wei and Tanner, 1990) was recently proposed by Van Leemput et al. (2003).”
A fully automatic and robust brain MRI tissue classification method (2003, positive): “Fully automatic, robust tissue classification is (1996), and further improved by many others (e.g. Ashrequired for the quantitative analysis of MRI data from burner, 2000; Ashburner and Friston, 2000; Guillemaud large-scale (150–1000 subjects), possibly multi-site cliniand Brady, 1997; Held et al., ”
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2021, neutral): “Therefore, there is a clear need for a fast, accurate, and reproducible automated method, for segmentation of brain scans of any contrast and resolution, and that can adapt to a wide range of populations.”
Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images (2005, background): “As assumed by most authors [21], the mixtures CW and CGW are not considered because they are so quantitatively insignificant that and are not relevant in explaining .”
The Optimal Template Effect in Hippocampus Studies of Diseased Populations (2009, background): “…a standardized neuroanatomical space for clinical imaging studies (Ashburner et al., 1998; Thompson et al., 2000) and spatial prior probabilities for a variety of segmentation methods (Zhang et al., 2001; Leemput et al., 2003; Prastawa et al., 2005; Awate et al., 2006; Ashburner and Friston, 2009).”