title: 2D/3D fetal cardiac dataset segmentation using a deformable model.
abstract: OBJECTIVE
  To segment the fetal heart in order to facilitate the 3D assessment of the cardiac function and structure.
  
  
  METHODS
  Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. The authors outline a level set deformable model to automatically delineate the small fetal cardiac chambers. The level set is penalized from growing into an adjacent cardiac compartment using a novel collision detection term. The region based model allows simultaneous segmentation of all four cardiac chambers from a user defined seed point placed in each chamber.
  
  
  RESULTS
  The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm's delineation and manual tracings are within 2 mm which is less than 10% of the length of a typical fetal heart. The ejection fractions were determined from the 3D datasets. We validate the algorithm using a physical phantom and obtain volumes that are comparable to those from physically determined means. The algorithm segments volumes with an error of within 13% as determined using a physical phantom.
  
  
  CONCLUSIONS
  Our original work in fetal cardiac segmentation compares automatic and manual tracings to a physical phantom and also measures inter observer variation.
authors.affiliation: Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, Bern, Switzerland. irving.dindoyal@istb.unibe.ch
authors.forename: Irving
authors.initials: I
authors.lastname: Dindoyal
authors.forename: Tryphon
authors.initials: T
authors.lastname: Lambrou
authors.forename: Jing
authors.initials: J
authors.lastname: Deng
authors.forename: Andrew
authors.initials: A
authors.lastname: Todd-Pokropek
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016216:Ultrasonography, Prenatal / Q000379:methods*