title: Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.
abstract: Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.
authors.affiliation: Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario N6A 5C1, Canada.
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario.|Cytognomix Inc, 60 North Centre Rd., Box 27052, London, Ontario N5X 3X5, Canada.
authors.forename: Joan H
authors.initials: JH
authors.lastname: Knoll
authors.affiliation: Consumer and Clinical Radiation Protection Bureau, Health Canada, 775 Brookfield Rd., K1A 1C1, Ottawa, Ontario, Canada.
authors.forename: Ruth C
authors.initials: RC
authors.lastname: Wilkins
authors.affiliation: Biodosimetry Emergency Response Laboratory, Canadian Nuclear Laboratories, 20 Forest Avenue, Deep River, ON K0J 1P0.
authors.forename: Farrah N
authors.initials: FN
authors.lastname: Flegal
authors.affiliation: Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario N6A 5C1, Canada.|Cytognomix Inc, 60 North Centre Rd., Box 27052, London, Ontario N5X 3X5, Canada.
authors.forename: Peter K
authors.initials: PK
authors.lastname: Rogan
mesh_terms: D000465:Algorithms; D000818:Animals; D002503:Centromere / Q000235:genetics; D002869:Chromosome Aberrations; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning
keywords: biodosimetry; cytogenetics; radiation exposure; software development; support vector machines