Towards Safer, Faster Prenatal Genetic Tests: Novel Unsupervised, Automatic and Robust Methods of Segmentation of Nuclei and Probes.

In this paper we present two new methods of segmentation that we developed for nuclei and chromosomic probes - core objects for cytometry medical imaging. Our nucleic segmentation method is mathematically grounded on a novel parametric model of an image histogram, which accounts at the same time for the background noise, the nucleic textures and the nuclei's alterations to the background. We adapted an Expectation-Maximisation algorithm to adjust this model to the histograms of each image and subregion, in a coarse-to-fine approach. The probe segmentation uses a new dome-detection algorithm, insensitive to background and foreground noise, which detects probes of any intensity. We detail our two segmentation methods and our EM algorithm, and discuss the strengths of our techniques compared with state-of-the-art approaches. Both our segmentation methods are unsupervised, automatic, and require no training nor tuning: as a result, they are directly applicable to a wide range of medical images. We have used them as part of a large-scale project for the improvement of prenatal diagnostic of genetic diseases, and tested them on more than 2,100 images with nearly 14,000 nuclei. We report 99.3% accuracy for each of our segmentation methods, with a robustness to different laboratory conditions unreported before. [ABSTRACT FROM AUTHOR]