title: Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images.
abstract: BACKGROUND
  The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1-2 mm by a level set image segmentation methodology.
  
  
  METHODS
  Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (n = 8) obtained ex situ. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics.
  
  
  CONCLUSIONS
  The mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1-2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early.
  
  
  CONCLUSIONS
  The hypothesis that level set segmentation can be accurate to within 1-2 mm on average was supported, although there can be some greater deviation. The method was robust to boundary leakage as evidenced by the high specificity. It was concluded that the technique is promising and that a suitable data set of human ovarian images should be obtained to conduct further studies.
authors.affiliation: Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. brennan.rusnell@usask.ca
authors.forename: Brennan J
authors.initials: BJ
authors.lastname: Rusnell
authors.forename: Roger A
authors.initials: RA
authors.lastname: Pierson
authors.forename: Jaswant
authors.initials: J
authors.lastname: Singh
authors.forename: Gregg P
authors.initials: GP
authors.lastname: Adams
authors.forename: Mark G
authors.initials: MG
authors.lastname: Eramian
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D003338:Corpus Luteum / Q000000981:diagnostic imaging*; D005260:Female; D005583:Fourier Analysis; D007091:Image Processing, Computer-Assisted; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*