Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

By Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Joseph V. Hajnal, Daniel Rueckert. NeuroImage, 2009

947 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/99f6643014df5ea1e97391718fd8d44fb3715000

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

A supervised patch-based approach for human brain labeling (2011, positive): “The keypoints of registrationbased label propagation approaches concern the accuracy of the nonrigid registration, the fusion rules [11], [30], [36], [15], [3], the selection of the labeled images [37], [2], [35] and the labeling errors in primary manual segmentation.”
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation (2016, neutral): “3) Multiatlas registration based methods [1, 2, 28].”
Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment (2014, methodology): “The unique feature of JLF compared to earlier multi-atlas segmentation methods that use weighted voting [Aljabar et al., 2009; Artaechevarria et al., 2009; Heckemann et al., 2010; Landman and Warfield, 2012; Sabuncu et al., 2010] is that when atlas weights are computed, an attempt is made to…”
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, positive): “Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al.”
Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis (2015, methodology): “More sophisticated approaches, such as multi-atlases (Aljabar, 2009; Heckemann, 2006; Iosifescu, 1997; Jia et al., 2012; Klein and Hirsch, 2005; Lao, 2004; Liu et al., 2004; Rohl ﬁ ng et al., 2004; Wang, 2010; War ﬁ eld et al., 2004; Wu et al., 2007), may increase the accuracy in the future.”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “817 – – – – 18v (IBSR) Aljabar [65] 0.”
Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates (2012, methodology): “Similarly, others have demonstrated that a label-fusion strategy weighted towards an optimal combination of subjects in the template library will improve segmentation [Aljabar et al., 2007, 2009; Heckemann et al., 2006; Rohlﬁng et al., 2004a,b].”
Multi-Atlas Segmentation with Joint Label Fusion (2012, methodology): “Multi-atlas construction is done either by constructing one representative atlas for each mode obtained from clustering training images [5], [2], [32] or by simply selecting the most relevant atlases for the unknown image on-the-fly [30], [1], [41].”
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration (2013, positive): “In atlas-based segmentation, the registered atlas (or combination of registered atlases) can be treated as a final segmentation as in [64], [65].”
101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol (2012, neutral): “However,surface-basedlabelingprotocolsavoidthe useofcuttingplanesthatarbitrarilycutthroughavolumetocon-nectlandmarks”
Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates (2014, methodology): “This result suggests that using a weighted voting strategy does not significantly improve MAGeT-Brain segmentation agreement, contrary to thefindings of Aljabar et al. (2009) for basicmulti-atlas segmentation.”
A Generative Model for Image Segmentation Based on Label Fusion (2010, positive): “To alleviate this problem, various heuristics for selecting only a subset of the training subjects for use in registration and label fusion have been proposed [1].”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “…approach employs a common coordinate system, similar to conventional probabilistic atlas segmentation methods, either via a standard template (Aljabar et al., 2007, 2009), a population average (Artaechevar-ria et al., 2008; Commowick and Malandain, 2007; Commowick et al., 2009; Depa et al.,…”
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation (2011, methodology): “…et al., 1998; Shen et al., 2002), appearance-based models (Duchesne et al., 2002; Hu and Collins, 2007), and atlas/template-warping techniques (Aljabar et al., 2009; Barnes et al., 2008; Collins et al., 1995; Fischl et al., 2002; Gousias et al., 2008; Hammers et al., 2007; Heckemann et al.,…”
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018, neutral): “an image-to-image similarity metric [25]–[28].”