title: Evaluation of segmentation algorithms on cell populations using CDF curves.
abstract: Cell segmentation is a critical step in the analysis pipeline for most imaging cytometry experiments and evaluating the performance of segmentation algorithms is important for aiding the selection of segmentation algorithms. Four popular algorithms are evaluated based on their cell segmentation performance. Because segmentation involves the classification of pixels belonging to regions within the cell or belonging to background, these algorithms are evaluated based on their total misclassification error. Misclassification error is particularly relevant in the analysis of quantitative descriptors of cell morphology involving pixel counts, such as projected area, aspect ratio and diameter. Since the cumulative distribution function captures completely the stochastic properties of a population of misclassification errors it is used to compare segmentation performance.
authors.affiliation: Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. hagwood@nist.gov
authors.forename: Charles
authors.initials: C
authors.lastname: Hagwood
authors.forename: Javier
authors.initials: J
authors.lastname: Bernal
authors.forename: Michael
authors.initials: M
authors.lastname: Halter
authors.forename: John
authors.initials: J
authors.lastname: Elliott
mesh_terms: D000465:Algorithms; D058948:Cell Tracking / Q000379:methods*; D003627:Data Interpretation, Statistical; D005434:Flow Cytometry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique