Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains

By Torsten Rohlfing, Robert Brandt, Randolf Menzel, Calvin R. Maurer Jr.. NeuroImage, 2004

641 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/926680ae8253d29a0b575b55d8e4b5485faa6757

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In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017, neutral): “23, 81369 München, Germany; Email: christian.wachinger@med.unimuenchen.de Pohl et al., 2006; Heckemann et al., 2006; Rohlfing et al., 2004, 2005; Svarer et al., 2005; Sabuncu et al., 2010; Asman and Landman, 2012; Wang et al., 2013b; Wachinger and Golland, 2014).”
Review of brain MRI image segmentation methods (2010, methodology): “In Caselles et al. (1997), P1 and P2 are determined by nonparametric Parzen density estimates (Ren and He 2007), which are computed by means of smoothed histograms of the regions (Rousson et al. 2003).”
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines (2007, background): “The sensitivity of the overlap measure depends on the surface-volume ratio of the objects [ 46 ].”
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI (2016, methodology): “This competition highlights the current interest in methods based on the multi-atlas segmentation framework (Rohlfing et al., 2004; Klein et al., 2005; Heckemann et al., 2006), which is becoming one of the most widely used and successful image segmentation techniques in medical imaging applications…”
Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest (2008, methodology): “…atlases, through non-rigid registrations using free form deformations (Rueckert et al., 1999), label propagation (Bajcsy et al., 1983; Miller et al., 1993) and decision fusion (Rohlfing et al., 2004), implementing an automated method used for adult brain segmentation (Heckemann et al., 2006).”
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].”
volBrain: An Online MRI Brain Volumetry System (2015, background): “…label fusion segmentation techniques has gained in popularity because they can combine multiple atlas information, thereby minimizing mislabeling from inaccurate aﬃne or non-linear registration (Rohlﬁng et al., 2004; Heckemann et al., 2006; Collins and Pruessner, 2010; Lötjönen et al., 2010).”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, methodology): “This approach has been shown to be more accurate than an individual atlas segmentation [316] in the same manner as a combination of classiﬁers is generally more accurate than a single classiﬁer in many pattern recognition scenarios [325-330] .”
Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans (2009, methodology): “To overcome these potential problems and to make the method more robust, Rohlfing et al. [ 3 ] and Heckemann et al. [20] proposed to register a number of manually segmented images, referred to as atlases, to the target image.”
Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy (2009, positive): “Rohlfing et al. (2004a) used a database of images of bee brains to show that fusing segmentations using majority voting is robust and accurate compared with, for example, the propagation of an average shape atlas, or of an individual atlas, selected according to its similarity to the query image.”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “For example, in atlas-based segmentation methods [7], [8] the transformation is applied to a segmentation (label image) of the moving image.”
A Generative Model for Image Segmentation Based on Label Fusion (2010, positive): “A detailed parcellation of the brain anatomy into structurally or functionally defined regions of interest (ROI) typically requires supervision, commonly in the form of labeled training data, since the local appearance of most such structures is not easily distinguishable [23], [30], [40], [51], [72”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, background): “Note that, as shown in (Rohlfing et al., 2004), the Jaccard overlap values of different structures is affected by their surface to volume ratio.”
Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data (2009, methodology): “HE PRINCIPLES of atlas-based segmentation have been successfully applied to a wide variety of image modalities and segmentation tasks [1]–[ 5 ].”