Magnetic resonance image tissue classification using a partial volume model.

By David W. Shattuck, Stephanie Sandor-Leahy, Kirt Schaper, David A. Rottenberg, Richard M. Leahy. NeuroImage, 2001

1,052 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b7d1dc8653a16ad8ebbc0295eb83ff9e5abc4a45

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

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable (2011, methodology): “The Dice coefficient [20] of volume overlap, which is also frequently reported [21], can be readily obtained from the Jaccard index via (see [22]); thus the relative comparison between the different methods remains unaffected when representing these results as Dice coefficients.”
Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children (2004, positive): “Each image data set was skull stripped, tissue segmented, and inhomogeneity corrected using automated software (Shattuck et al., 2001).”
The electric field in the cortex during transcranial current stimulation (2013, methodology): “The segmentation of these 1×1×1 mm3 resolution images was performed using Brainsuite (version 9.01, http://www.loni.ucla.edu/Software/ BrainSuite) (Dogdas et al., 2005; Shattuck and Leahy, 2001, 2002; Shattuck et al., 2001), with some adjustments of the default parameter values.”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “Other approaches for the bias field correction include minimizing the image entropy [32], fitting the histogram of the local neighbourhood to global histogram of the image [28], maximizing the high-frequency content of the image [26], and using a registered template image [27].”
SynthStrip: skull-stripping for any brain image (2022, positive): “…increases the reliability of linear registration (Cox and Jesmanowicz, 1999; Friston et al., 1995; Hoﬀmann et al., 2015; Jenkinson and Smith, 2001; Jiang et al., 1995; Modat et al., 2014; Reuter et al., 2010) by excluding anatomy that deforms non-rigidly, such as the eyes, jaw, and…”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, methodology): “• Another popular method is Brain Surface Extraction (BSE) [2].”
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, methodology): “U N C O R R E C Additional capabilities include handling ofmultivariate data, partial volume modeling (Shattuck et al., 2001), a memory-minimization mode, label propagation, a plug-and-play architecture for incorporation of novel likelihood models which include both parametric and nonparametric mode”
Fast and robust parameter estimation for statistical partial volume models in brain MRI (2004, methodology): “In general, there are three approaches to the parameter estimation problem: histogram analysis (Santago and Gage, 1993), simultaneous parameter, and partial volume estimation by expectation maximization (EM)-like algorithms (Noe and Gee, 2001), and estimation based on a hard segmentation of the imag”
Improved watershed transform for medical image segmentation using prior information (2004, background): “In [31], a classification step is combined with spatial information, and partial volume effects are also considered in [35].”
A review on automatic fetal and neonatal brain MRI segmentation (2017, positive): “Brain extraction techniques used for the adult brain, such as BET11 (Smith, 2002) and BSE14 (Shattuck et al., 2001), are often successful for the neonatal brain and have been used in the literature (Anbeek et al., 2008; Srhoj-Egekher et al., 2012; Ledig et al., 2012; Makropoulos et al., 2012b; Moesk”
A hybrid approach to the skull stripping problem in MRI (2004, neutral): “, 1999), Hahn and Peitgen’s (2000) watershed algorithm (denoted WAT), and Brain Surface Extractor (BSE in Shattuck and Leahy, in press; Shattuck et al., 2001).”
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping (2016, neutral): “2014; Shattuck et al., 2001; Wang et al., 2010; Zhang et al., 2001; Zhao et al., 2010).”
Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images (2005, methodology): “Recently, Grau et al. [30] proposed an improved watershed method using prior information and they compare their approach for WM and GM segmentation with the methods of Van Leemput et al. [24] and Zeng et al. [31].”
Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest (2008, methodology): “We validated the approach of constructing atlases of the 2- year-old brain by means of overlap ratios (OR) (Jaccard, 1907) and similarity indices (SI) (Dice, 1945; Shattuck et al., 2001) between the automated and manual delineation of representative structures.”
Handbook of Functional MRI Data Analysis: Index (2011, background): “A more complex approach combines bias field correction with tissue segmentation; different classes of tissue (gray matter, white matter, and CSF) are modeled, and the algorithm attempts to equate the distributions of intensities in these tissue classes across different parts of the brain (Sled et al”