title: Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases.
abstract: OBJECTIVE
  Automated segmentation is required for radiotherapy treatment planning, and multi-atlas methods are frequently used for this purpose. The combination of multiple intermediate results from multi-atlas segmentation into a single segmentation map can be achieved by label fusion. A method that includes expert knowledge in the label fusion phase of multi-atlas-based segmentation was developed. The method was tested by application to prostate segmentation, and the accuracy was compared to standard techniques.
  
  
  METHODS
  The selective and iterative method for performance level estimation (SIMPLE) algorithm for label fusion was modified with a weight map given by an expert that indicates the importance of each region in the evaluation of segmentation results. Voxel-based weights specified by an expert when performing the label fusion step in atlas-based segmentation were introduced into the modified SIMPLE algorithm. These weights incorporate expert knowledge on accuracy requirements in different regions of a segmentation. Using this knowledge, segmentation accuracy in regions known to be important can be improved by sacrificing segmentation accuracy in less important regions. Contextual information such as the presence of vulnerable tissue is then used in the segmentation process. This method using weight maps to fine-tune the result of multi-atlas-based segmentation was tested using a set of 146 atlas images consisting of an MR image of the lower abdomen and a prostate segmentation. Each image served as a target in a set of leave-one-out experiments. These experiments were repeated for a weight map derived from the clinical practice in our hospital.
  
  
  RESULTS
  The segmentation accuracy increased 6 % in regions that border vulnerable tissue using expert-specified voxel-based weight maps. This was achieved at the cost of a 4 % decrease in accuracy in less clinically relevant regions.
  
  
  CONCLUSIONS
  The inclusion of expert knowledge in a multi-atlas-based segmentation procedure was shown to be feasible for prostate segmentation. This method allows an expert to ensure that automatic segmentation is most accurate in critical regions. This improved local accuracy can increase the practical value of automatic segmentation.
authors.affiliation: Image Sciences Institute, University Medical Center, Utrecht, The Netherlands, t.langerak@erasmusmc.nl.
authors.forename: T R
authors.initials: TR
authors.lastname: Langerak
authors.forename: U A
authors.initials: UA
authors.lastname: van der Heide
authors.forename: A N T J
authors.initials: AN
authors.lastname: Kotte
authors.forename: F F
authors.initials: FF
authors.lastname: Berendsen
authors.forename: M
authors.initials: M
authors.lastname: van Vulpen
authors.forename: J P W
authors.initials: JP
authors.lastname: Pluim
mesh_terms: D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011467:Prostate / Q000473:pathology*; D015203:Reproducibility of Results
references.citation: Int J Radiat Oncol Biol Phys. 2004 Apr 1;58(5):1616-34
references.pmid: 15050343
references.citation: IEEE Trans Med Imaging. 1999 Aug;18(8):712-21
references.pmid: 10534053
references.citation: Neuroimage. 2009 Jul 1;46(3):726-38
references.pmid: 19245840
references.citation: Med Phys. 2008 Apr;35(4):1407-17
references.pmid: 18491536
references.citation: IEEE Trans Med Imaging. 2009 Jun;28(6):838-46
references.pmid: 19272988
references.citation: IEEE Trans Med Imaging. 2012 Jun;31(6):1326-36
references.pmid: 22438513
references.citation: IEEE Trans Med Imaging. 2009 Aug;28(8):1266-77
references.pmid: 19228554
references.citation: Med Image Anal. 2010 Feb;14(1):39-49
references.pmid: 19897403
references.citation: Neuroimage. 2004 Apr;21(4):1428-42
references.pmid: 15050568
references.citation: IEEE Trans Med Imaging. 2010 Dec;29(12):2000-8
references.pmid: 20667809
references.citation: IEEE Trans Med Imaging. 2004 Jul;23(7):903-21
references.pmid: 15250643
references.citation: Neuroimage. 2006 Oct 15;33(1):115-26
references.pmid: 16860573
references.citation: Eur J Radiol. 2007 Sep;63(3):361-8
references.pmid: 17683887
references.citation: IEEE Trans Med Imaging. 2009 Jul;28(7):1000-10
references.pmid: 19131298