Current methods in medical image segmentation.

By Dzung L. Pham, Chenyang Xu, Jerry L Prince. Annual Review of Biomedical Engineering, 2000

2,570 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9b0d18a9d1faa618d466fef25ef36f1ddc02030b

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Citations in the corpus

Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, background): “In this context, the classical atlas-guided approach treats segmentation as an image registration problem (Pham et al., 2000), where spatial correspondence is established between the atlas and novel image coordinates.”
A review of vessel extraction techniques and algorithms (2004, methodology): “[2000] published a book chapter on medical image segmentation using deformable models and another book chapter on current methods in medical image segmentation [Pham et al. 2000] which includes a section on deformable models.”
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data (2011, background): “The chronology of progress in segmentation may be tracked through both technical reviews (Bezdek et al. 1993; Pal and Pal 1993; Clarke et al. 1995; Pham et al. 2000; Viergever et al. 2001; Suri et al. 2002; Duncan et al. 2004; Balafar et al. 2010) and evaluation studies (e.”
Geodesic Shooting for Computational Anatomy (2006, background): “This second point of view in terms of the conservation of momentum law also sheds new light on a large number of high dimensional evolution based Active Model Methods in Computer Vision, including active snakes and contours [6, 12, 18, 20, 29, 31, 37, 40, 41, 43], active surfaces and deformable mode”
The ImageJ ecosystem: an open platform for biomedical image analysis (2015, background): “``ImageJ's extensible structure means new functionality can always be layered on top of the existing tools rather than being created from the ground up.''”
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019, methodology): “In the early days, simple rule-based approaches were followed; however, those methods failed to maintain robustness when tested on huge variety of data [7].”
Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification (2007, background): “Pham et al. (2000) provides a more comprehensive listing of all the work carried out in this area.”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, background): “Classification means to assign to each element in the image a tissue class, where the classes are defined in advance.”
Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures (2009, methodology): “More detailed reviews are presented in the work of Pal and Pal [1993], Pham et al. [2000] , Sezgin and Sankur [2004], and Wirjadi [2007].”
Fractal and multifractal analysis: A review (2009, background): “Many research groups worked to solve specific medical image segmentation problems, with good results (Duncan and Ayache, 2000; Pham et al., 1999); however, until now, the generalization of these systems to a wider range of applications has not been successful.”
Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment (2013, neutral): “These include climate/meteorology [ Beaulieu et al. , 2008; Gazeaux et al. , 2011], bio-statistics research [ Olshen et al. , 2004], image processing [ Pham et al. , 2000], and quantitative marketing [ Fong and DeSarbo , 2007].”
Artificial intelligence in radiology (2018, positive): “Simpler segmentation algorithms used clustered imaging intensities to isolate different areas or utilized region growing, where regions are expanded around user-defined seed points within objects until a certain homogeneity criterion is no longer met 48 .”
A survey of MRI-based medical image analysis for brain tumor studies (2013, background): “Regarding tumor angiogenesis, current understanding is based on the fact that low-grade gliomas are moderately vascularized tumors, whereas highgrade gliomas show prominent microvascular proliferations and areas of high vascular density (Plate and Risau (1995)).”