Abstract
Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.
Highlights
Analysis of microscopy images of metaphase cells demonstrates the damaging effects of ionizing radiation and can be used to measure the amount of radiation absorbed
Application of chromosome morphology filters to remove false positive (FP) False positive dicentric chromosomes (DCs) (n=98) from a set of metaphase cells exposed to low dose radiation were classified into morphological subclasses to identify and eliminate these objects
FPs could be eliminated for each morphological subclass (Table S1), with most of the segmentation filters acting on their targeted subclass
Summary
Analysis of microscopy images of metaphase cells demonstrates the damaging effects of ionizing radiation and can be used to measure the amount of radiation absorbed. While some aspects of the assay have been successfully streamlined, the overall throughput remains limited by the labour-intensive identification of DCs in many cells This affects the timely estimation of radiation exposure, especially for testing multiple affected individuals in a large accident or a mass casualty nuclear event. Image quality assessment has traditionally compared new data relative to reference images, complex mathematical models, or distortions from a training set recognized by machine learning. Image quality assessment has traditionally compared new data relative to reference images, complex mathematical models, or distortions from a training set recognized by machine learning5 Such generic approaches are not appropriate in the DCA because features tailored for ranking morphologically diverse chromosome images are not generalized as entropic or other measures applying frequency filters to intensity distributions. We demonstrate that quality chromosomal images can be selected for the DCA using supervised, image segmentation rules aimed at categorizing the preferred images and eliminating false positive (FP) DCs
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