Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans

By Ivana Isgum, Marius Staring, Annemarieke Rutten, Mathias Prokop, Max A. Viergever, B. van Ginneken. IEEE Transactions on Medical Imaging, 2009

429 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/af1ac5542db7aab4911c3f70b080564c83d4d0cc

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Multi-Atlas Segmentation with Joint Label Fusion (2012, positive): “The inverse distance weighting has been applied as well [3], [17] 2 : w i ðxÞ ¼ 1 ZðxÞ X y2N ðxÞ F T ðyÞ À F i ðyÞ ð Þ 2 2 4 3 5 À ;”
A supervised patch-based approach for human brain labeling (2011, neutral): “The labels are then mapped and fused using classification rules to produce a new label map of the input image [30], [15], [3], [2], [16], [8], [33], [35], [26], [38], [34], [25], [19].”
Medical image registration: a review (2014, background): “…Huang et al. 2009; King et al. 2010); anatomy segmentation (Collins and Evans 1997; Frangi et al. 2003; Dornheim et al. 2005; Martin et al. 2008; Isgum et al. 2009; Gao et al. 2010; Zhuang et al. 2010; Oliveira et al. 2011b); computational model building (Grosland et al. 2009) and image…”
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, positive): “, 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2013a) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the target image.”
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (2019, methodology): “Atlas-based SystemsAtlas-based registration [35], aswell as multi-atlas-based registration methods [2], [36], can also be applied to solve localization tasks.”
Regression Forests for Efficient Anatomy Detection and Localization in CT Studies (2010, methodology): “Robustness is improved by multi-atlas techniques [17], at the price of slower algorithms involving multiple registrations.”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “The earliest examples of this strategy used weights inversely proportional to the absolute difference between local intensities of the novel image and deformed atlas (Isgum et al., 2009; Iglesias and Karssemeijer, 2009), and standard local intensity-based registration metrics such as local cross-cor”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “These strategies include using the whole image to determine a single, global weight for each training subject [6], employing local image intensities for locally adapting the weights [6], [32] and using estimates of classifier performance in determining a weight for each label and training subject [5”