Towards large scale automated interpretation of cytogenetic biodosimetry data
Cytogenetic biodosimetry is the definitive test for assessing exposure to ionizing radiation. It involves manual assessment of the frequency of dicentric chromosomes (DCs) on a microscope slide, which potentially contains hundreds of metaphase cells. We developed an algorithm that can automatically and accurately locate centromeres in DAPI-stained metaphase chromosomes and that will detect DCs. In this algorithm, a set of 200-250 metaphase cell images are ranked and sorted. The 50 top-ranked images are used in the triage DC assay (DCA). To meet the requirement of DCA in a mass casualty event, we are accelerating our algorithm through parallelization. In this paper, we present our finding in accelerating our ranking and segmentation algorithms. Using data parallelization on a desktop system, the ranking module was up to 4-fold faster than the serial version and the Gradient Vector Flow module (GVF) used in our segmentation algorithm was up to 8-fold faster. Large scale data parallelization of the ranking module processed 18,694 samples in 11.40 hr. Task parallelization of Image ranking with parallelized labeling on a desktop computer reduced processing time by 20% of a serial process, and GVF module recoded with parallelized matrix inversion reduced time by 70%. Overall, we estimate that the automated DCA will require around 1 min per sample on a 64-core computing system. Our long-term goal is to implement these algorithms on a high performance computer cluster to assess radiation exposures for thousands of individuals in a few hours.
|Conference||2012 IEEE 6th International Conference on Information and Automation for Sustainability, ICIAFS 2012|
Li, Y. (Yanxin), Wickramasinghe, A. (Asanka), Subasinghe A., A. (Akila), Samarabandu, J. (Jagath), Knoll, J. (Joan), Wilkins, R.C, … Rogan, P. (Peter). (2012). Towards large scale automated interpretation of cytogenetic biodosimetry data. In ICIAFS 2012 - Proceedings: 2012 IEEE 6th International Conference on Information and Automation for Sustainability (pp. 30–35). doi:10.1109/ICIAFS.2012.6420039