Automatic segmentation of MR images of the developing newborn brain

By Marcel Prastawa, John H. Gilmore, Weili Lin, Guido Gerig. Medical Image Anal., 2005

373 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/b6324a86003abd1b3206843151b9d34cf94f5f12

How researchers see this paper

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

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Pediatrics, Perinatology and Child Health

Citations in the corpus

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018, background): “, 2005 [17] T1,T2 Infant brain tissue Multi-atlas”
A review on automatic fetal and neonatal brain MRI segmentation (2017, neutral): “Parametric models solve the segmentation problem by fitting a model to the data.”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, background): “It is important to note that atlas-based MRI segmentation of the neonatal brain has become a research focus in recent years [42, 51, 53, 88].”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “Some groups proposed supervised classification methods [115–118] using both MR”
A Structural MRI Study of Human Brain Development from Birth to 2 Years (2008, methodology): “Image analysis presents multiple challenges including low contrast to noise ratio, high variability and small size of anatomical shapes, rapid changes as a function of age, and contrast changes and brightness inhomogeneities attributable to myelination (Prastawa et al., 2005).”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “Such supervised segmentation tools are commonly used in many medical imaging applications, including surgical planning [27] and the study of disease progression, aging or healthy development [23], [50], [74].”
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction (2017, methodology): “Furthermore, immature myelination of the white matter in neonatal and fetal brains results in inversion of MRI contrast when compared to adult brain scans (Prastawa et al., 2005).”
Deep Learning in Medical Image Analysis (2017, methodology): “Although many methods have been proposed for infant brain image segmentation, most of those focused on segmentation of either neonatal images (~3 months) or infant images (>12 months) using a single T1-weighted or T2-weighted image (102, 103, 104, 105, 106).”
Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest (2008, methodology): “5 years, the MR image contrast is similar to that in adults (Prastawa et al., 2005).”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, neutral): “…2003; Ferrie et al., 1999; Gao et al., 2009; Gousias et al., 2008; Huppi et al., 1998; Laule et al., 2007; Ment et al., 2009; Paus et al., 1999, 2001; Prastawa et al., 2005, 2010; Serag et al., 2011; Shi et al., 2010a,b; van der Knaap and Valk, 1990; van der Knaap et al., 1991; Xue et al., 2007).”
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data (2011, background): “Prastawa et al. 2005; Weisenfeld and Warfield 2009).”
A dynamic 4D probabilistic atlas of the developing brain (2011, methodology): “Most of the neonatal segmentation methods in the literature currently require a probabilistic atlas (Prastawa et al., 2005; Weisenfeld and Warfield, 2009), or manual segmentations as a spatial prior (Anbeek et al., 2006).”