Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

By Xabier Artaechevarria, Arrate Muñoz-Barrutia, Carlos Ortiz-de-Solorzano. IEEE Transactions on Medical Imaging, 2009

557 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5f6f78080a42d5ac6bccb4bbfbdbb0e854666a85

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In Biomedical Engineering

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, unknown): “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.”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “Finally, a classiﬁcation map for the test case is inferred as a combination of the retrieved labels during a label fusion step [94]–[97].”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “, 2009; Iglesias and Karssemeijer, 2009), and standard local intensity-based registration metrics such as local cross-correlation (Artaechevarria et al., 2009).”
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 estimat”
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation (2016, unknown): “3) Multiatlas registration based methods [1, 2, 28].”
Multi-Atlas Segmentation with Joint Label Fusion (2012, methodology): “Most existing label fusion methods are based on weighted voting [30], [16], [3], [17], [33], where each atlas contributes to the final solution according to a nonnegative weight, with atlases more similar to the target image receiving larger weights.”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “758 275v b Artaechevarria [68] 0.”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, unknown): “For the purposes of reviewing the existing methods for atlas propagation and atlas fusion in multi-atlas based segmentation, a similar notation to the adopted ones in [318, 319] will be used.”
Fast and robust multi-atlas segmentation of brain magnetic resonance images (2010, background): “Results are also comparable when the accuracy of multiatlas segmentation is compared with three other previously published methods (Artaechevarria et al., 2009; Han and Fischl, 2007; Heckemann et al., 2006). Detailed comparisons with Han and Fischl (2007) and Heckemann et al. (2006) are not possible”
A review on automatic fetal and neonatal brain MRI segmentation (2017, unknown): “Different voting schemes have been proposed that weight the contribution of each atlas according to the similarity of the atlas MR image to the unseen image, either globally or locally (Artaechevarria et al., 2009). Patch-based techniques (Coupé et al., 2011; Rousseau et al., 2011) provide a non-loc”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, unknown): “(3) Multi-atlas registration based methods (Klein and Hirsch, 2005; Aljabar et al., 2009; Artaechevarria et al., 2009; Sarikaya et al., 2013; Habas et al., 2010; Shi et al., 2010).”
SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration (2016, methodology): “Elastix has been applied to many problems including multi-atlas segmentation of CT and MRI images [1], early diagnosis of dementia [6], estimation of pulmonary emphysema [25], groupwise registration [22], 2D-3D registration in x-ray guided interventions [26], ra-”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “The software has been used in several research projects, including [2], [8], [15], [24], [50], [55], [67]–[73], and is still under active development.”