title: Out-of-atlas likelihood estimation using multi-atlas segmentation.
abstract: OBJECTIVE
  Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures that are anatomically consistent across a large population of potential target subjects (i.e., multi-atlas segmentation is limited to "in-atlas" applications). Herein, the authors propose a technique to determine the likelihood that a multi-atlas segmentation estimate is representative of the problem at hand, and, therefore, identify anomalous regions that are not well represented within the atlases.
  
  
  METHODS
  The authors derive a technique to estimate the out-of-atlas (OOA) likelihood for every voxel in the target image. These estimated likelihoods can be used to determine and localize the probability of an abnormality being present on the target image.
  
  
  RESULTS
  Using a collection of manually labeled whole-brain datasets, the authors demonstrate the efficacy of the proposed framework on two distinct applications. First, the authors demonstrate the ability to accurately and robustly detect malignant gliomas in the human brain-an aggressive class of central nervous system neoplasms. Second, the authors demonstrate how this OOA likelihood estimation process can be used within a quality control context for diffusion tensor imaging datasets to detect large-scale imaging artifacts (e.g., aliasing and image shading).
  
  
  CONCLUSIONS
  The proposed OOA likelihood estimation framework shows great promise for robust and rapid identification of brain abnormalities and imaging artifacts using only weak dependencies on anomaly morphometry and appearance. The authors envision that this approach would allow for application-specific algorithms to focus directly on regions of high OOA likelihood, which would (1) reduce the need for human intervention, and (2) reduce the propensity for false positives. Using the dual perspective, this technique would allow for algorithms to focus on regions of normal anatomy to ascertain image quality and adapt to image appearance characteristics.
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA. andrew.j.asman@vanderbilt.edu
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Asman
authors.forename: Lola B
authors.initials: LB
authors.lastname: Chambless
authors.forename: Reid C
authors.initials: RC
authors.lastname: Thompson
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
references.citation: IEEE Trans Med Imaging. 2010 Oct;29(10):1714-29
references.pmid: 20562040
references.citation: IEEE Trans Med Imaging. 2011 Oct;30(10):1779-94
references.pmid: 21536519
references.citation: Magn Reson Med. 2013 Mar 1;69(3):891-902
references.pmid: 22611000
references.citation: IEEE Trans Med Imaging. 2002 Feb;21(2):139-49
references.pmid: 11929101
references.citation: Med Image Anal. 2013 Feb;17(2):194-208
references.pmid: 23265798
references.citation: Med Image Comput Comput Assist Interv. 2010;13(Pt 3):25-32
references.pmid: 20879379
references.citation: Med Image Comput Comput Assist Interv. 2011;14(Pt 3):322-9
references.pmid: 22003715
references.citation: Neuroimage. 2004 Apr;21(4):1428-42
references.pmid: 15050568
references.citation: J Cogn Neurosci. 2007 Sep;19(9):1498-507
references.pmid: 17714011
references.citation: IEEE Trans Med Imaging. 2004 Jul;23(7):903-21
references.pmid: 15250643
references.citation: IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):611-23
references.pmid: 22732662
references.citation: IEEE Trans Med Imaging. 2004 Aug;23(8):983-94
references.pmid: 15338732
references.citation: Med Image Comput Comput Assist Interv. 2012;15(Pt 3):426-34
references.pmid: 23286159
references.citation: Neuroimage. 2009 Jul 1;46(3):726-38
references.pmid: 19245840
references.citation: Neuroimage. 2011 Jan 15;54(2):940-54
references.pmid: 20851199
references.citation: Magn Reson Imaging. 2007 Oct;25(8):1196-202
references.pmid: 17442523
references.citation: IEEE Trans Med Imaging. 2012 Aug;31(8):1593-606
references.pmid: 22562727
references.citation: Inf Process Med Imaging. 2011;22:85-96
references.pmid: 21761648
references.citation: Magn Reson Med. 2009 Aug;62(2):365-72
references.pmid: 19526493
references.citation: Med Image Anal. 2004 Sep;8(3):275-83
references.pmid: 15450222
references.citation: Acad Radiol. 2003 Dec;10(12):1341-8
references.pmid: 14697002
references.citation: IEEE Trans Med Imaging. 2012 Feb;31(2):512-22
references.pmid: 22010145
references.citation: Inf Process Med Imaging. 2011;22:73-84
references.pmid: 21761647
references.citation: IEEE Trans Image Process. 2007 Jan;16(1):153-61
references.pmid: 17283774
references.citation: IEEE Trans Med Imaging. 2012 Jun;31(6):1326-36
references.pmid: 22438513
references.citation: Proc SPIE Int Soc Opt Eng. 2010 Mar 11;7628:
references.pmid: 24353379
references.citation: IEEE Trans Med Imaging. 2009 Aug;28(8):1266-77
references.pmid: 19228554
references.citation: Neuroimage. 2006 Oct 15;33(1):115-26
references.pmid: 16860573
references.citation: IEEE Trans Med Imaging. 2009 Jul;28(7):1000-10
references.pmid: 19131298
references.citation: Neuroimage. 2012 Apr 15;60(3):1819-31
references.pmid: 22500924
references.citation: Med Image Anal. 2008 Feb;12(1):26-41
references.pmid: 17659998
references.citation: IEEE Trans Med Imaging. 2008 May;27(5):629-40
references.pmid: 18450536
references.citation: Phys Med Biol. 2012 Jan 7;57(1):93-111
references.pmid: 22126838