Yanxin Li1, Joan H. Knoll2,3, Ruth Wilkins4, Farrah N. Flegal5, and Peter K. Rogan1,3* Automated Discrimination of Dicentric and Monocentric Chromosomes by Machine Learning-based Image Processing. Departments of 1Biochemistry, and 2Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, 3Cytognomix Inc., 4Health Canada, and 5Canadian Nuclear Laboratories.
in the journal Microscopy Research and Technique.
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). 16 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, sigma. At larger sigma, PPV decreases and TPR increases. At high dose, for sigma= 1.3, TPR = 0.52 and PPV = 0.83, while at sigma= 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and sigma = 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.
A preprint of the paper is available at bioRxiv: http://biorxiv.org/content/early/2016/01/19/037309