Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

Automating dicentric chromosome detection from cytogenetic biodosimetry data.

We present a prototype software system with sufficient capacity and speed to estimate radiation exposures in a mass casualty event by counting dicentric chromosomes (DCs) in metaphase cells from many individuals. Top-ranked metaphase cell images are segmented by classifying and defining chromosomes with an active contour gradient vector field (GVF) and by determining centromere locations along the centreline. The centreline is extracted by discrete curve evolution (DCE) skeleton branch pruning and curve interpolation. Centromere detection minimises the global width and DAPI-staining intensity profiles along the centreline. A second centromere is identified by reapplying this procedure after masking the first. Dicentrics can be identified from features that capture width and intensity profile characteristics as well as local shape features of the object contour at candidate pixel locations. The correct location of the centromere is also refined in chromosomes with sister chromatid separation. The overall algorithm has both high sensitivity (85 %) and specificity (94 %). Results are independent of the shape and structure of chromosomes in different cells, or the laboratory preparation protocol followed. The prototype software was recoded in C++/OpenCV; image processing was accelerated by data and task parallelisation with Message Passaging Interface and Intel Threading Building Blocks and an asynchronous non-blocking I/O strategy. Relative to a serial process, metaphase ranking, GVF and DCE are, respectively, 100 and 300-fold faster on an 8-core desktop and 64-core cluster computers. The software was then ported to a 1024-core supercomputer, which processed 200 metaphase images each from 1025 specimens in 1.4 h

Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes

Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection

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 \u3c0.2 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

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation

Live-Cell Imaging of Human Oocytes and Regulation of Cohesin Removal in Meiosis II

Ph. D. ThesisThe “maternal age effect” describes the striking increase in risk of miscarriage and chromosomally abnormal embryos and children from women older than 35. Studies in mice have shown that the protein complex Cohesin is reduced in an age-dependent manner. This protein complex ensures accurate segregation during both rounds of meiosis, by holding the chromosomes together and providing a counteracting force to spindle microtubules. Despite a wealth of knowledge generated from human oocytes, there are few live-cell studies, in part due to the paucity of material. This thesis uses human oocytes specifically donated for research to assess the effect of age on alignment of chromosomes at metaphase I and -II, which is a predictor of missegregation. Using high-resolution live-cell microscopy, it is clear that increased age is associated with chromosomes that are misaligned in metaphase I and -II. At metaphase II, eggs arrest until they are fertilised by sperm. The regulation of how chromosomes separate at this point is poorly understood. While the bulk of Cohesin is removed in anaphase I, a small amount is “protected” by Shugoshin 2 and remains between the centromeres to allow for faithful segregation in meiosis II. Currently, there is poor experimental work to support the hypotheses proposed to explain how the mechanisms that protect Cohesin in meiosis I are removed in meiosis II. One of these hypotheses is the requirement of spindle tension to separate the protector, Shugoshin 2, from Cohesin in meiosis II. Here, I show that spindle tension is not required for deprotection and that alternative models should be considered, such as one which suggests that higher-order regulation around meiosis II resumption is orchestrated by the Anaphase Promoting Complex and its coactivator Cdc20, APC/CCdc20, a protein complex that is active at anaphase onset

COMPREHENSIVE PERFORMANCE EVALUATION AND OPTIMIZATION OF HIGH THROUGHPUT SCANNING MICROSCOPY FOR METAPHASE CHROMOSOME IMAGING

Specimen scanning is a critically important tool for diagnosing the genetic diseases in today’s hospital. In order to reduce the clinician’s work load, many investigations have been conducted on developing automatic sample screening techniques in the last twenty years. However, the currently commercialized scanners can only accomplish the low magnification sample screening (i.e. under 10× objective lens), and still require clinicians’ manual operation for the high magnification image acquisition and confirmation (i.e. under 100× objective lens). Therefore, a new high throughput scanning method is recently proposed to continuously scan the specimen and select the clinically analyzable cells. In the medical imaging lab, University of Oklahoma, a prototype of high throughput scanning microscopy is built based on the time delay integration (TDI) line scanning detector. This new scanning method, however, raises several technical challenges for evaluating and optimizing the performance. First, we need to use the clinical samples to compare this new prototype with the conventional two-step scanners. Second, the system DOF should be investigated to assess the impact on clinically analyzable metaphase chromosomes. Further, in order to achieve the optimal results, we should carefully assess and select the auto-focusing methods for the high throughput scanning system. Third, we need to optimize the scanning scheme by finding the optimal trade-off between the image quality and efficiency. Finally, analyzing the performance of the various image features is meaningful for improving the performance of the computer aided detection (CAD) scheme under the high throughput scanning condition. The purpose of this dissertation is to comprehensively evaluate the performance of the high throughput scanning prototype. The first technical challenge was solved by the first investigation, which utilized a number of 9 slides from five patients to compare the detecting performance of the high throughput scanning prototype. The second and third studies were performed for the second technical challenge. In the second study, we first theoretically computed the DOF of our prototype and then experimentally measured the system DOF. After that, the DOF impact was analyzed using cytogenetic images from different pathological specimens, under the condition of two objective lenses of 60× (dry, N.A. = 0.95) and 100× (oil, N.A. = 1.25). In the third study, five auto-focusing functions were investigated using metaphase chromosome images. The performance of these different functions was compared using four widely accepted criteria. The fourth and fifth investigations were designed for the third technical challenge. The fourth study objectively assessed chromosome band sharpness by a gradient sharpness function. The sharpness of the images captured from standard resolution target and several pathological chromosomes was objectively evaluated by the gradient sharpness function. The fifth study presented a new slide scanning scheme, which only applies the auto-focusing operations on limited locations. The focusing position was adjusted very quickly by linear interpolation for the other locations. The sixth study was aimed for the fourth technical challenge. The study investigated 9 different feature extraction methods for the CAD modules applied on our high throughput scanning prototype. A certain amount of images were first acquired from 200 bone marrow cells. Then the tested features were performed on these images and the images containing clinically meaningful chromosomes were selected using each feature individually. The identifying accuracy of each feature was evaluated using the receiver operating characteristic (ROC) method. In this dissertation, we have the following results. First, in most cases, we demonstrated that the high throughput scanning can select more diagnostic images depicting clinically analyzable metaphase chromosomes. These selected images were acquired with adequate spatial resolution for the following clinical interpretation. Second, our results showed that, for the commonly used pathological specimens, the metaphase chromosome band patterns are clinically recognizable when these chromosomes were obtained within 1.5 or 1.0 μm away from the focal plane, under the condition of applying the two 60× or 100× objective lenses, respectively. In addition, when scanning bone marrow and blood samples, the Brenner gradient and threshold pixel counting methods can achieve the optimal performance, respectively. Third, we illustrated that the optimal scanning speed of clinical samples is 0.8 mm/s, for which the captured image sharpness is optimized. When scanning the blood sample slide with an auto-focusing distance of 6.9 mm, the prototype obtained an adequate number of analyzable metaphase cells. More useful cells can be captured by increasing the auto-focusing operations, which may be needed for the high accuracy diagnosis. Finally, we found that the optimal feature for the online CAD scheme is the number of the labeled regions. When applying the offline CAD scheme, the satisfactory results can be achieved by combining four different features including the number of the labeled regions, average region area, average region pixel value, and the standard deviation of the either region circularity or distance. Although these investigations are encouraging, there exist several limitations. First, the number of the specimens is limited in most of the assessments. Second, some important impacts, such as the DOF of human eye and the sample thickness, are not considered. Third, more recently proposed algorithms and image features are not used for the evaluation. Therefore, several further studies are planned, which may provide more meaningful information for improving the scanning efficiency and image quality. In summary, we believe that the high throughput scanning may be extensively applied for diagnosing genetic diseases in the future

HIGH-THROUGHPUT FLUORESCENCE MICROSCOPY FOR AUTOMATED CLINICAL APPLICATIONS

Fluorescence in situ hybridization (FISH) is a powerful tool for visualizing and detecting genetic abnormalities. Manual scoring FISH analysis is a tedious and labor-and-time-consuming task. Automated image acquisition and analysis provide an opportunity to overcome the difficulties. However, conventional fluorescence microscopes, the mostly used instrument for FISH imaging, have deficiencies. A multi-spectral image modality must be employed in order to visualize fluorescently dyed FISH probes for analysis, and the existing technologies are either two expensive, too slow, or both. Aiming at upgrading the current employed cytogenetic instrumentation, we developed a new imaging technique capable of simultaneously imaging multiple color spectra. Using the principle, we implemented a prototype system and conduct various characterization experiments. Experiment results (<1% peripheral geometric distortion, consistent signal response linearity, and ~2000 lp/mm spatial resolution) show no significant compromise in terms of optical performance. A detector alignment scheme was developed and performed to minimize registration error. The system has significantly faster acquisition speed than conventional fluorescence microscopes albeit the extra cost is quite insignificant

A FISTFUL OF MOLECULES: CELLS ESCAPE AN OPERATIONAL MITOTIC CHECKPOINT THROUGH A STOCHASTIC PROCESS

The cell cycle culminates with the segregation of sister chromatids, which is a fundamental step in ensuring the transmission of unaltered genetic material. Chromosome segregation is carried out by the mitotic spindle, which captures and pulls sister chromatids towards the opposite poles. Anaphase starts when the correct bipolar attachment is achieved. Chromosomes migrate evenly to the two daughter cells, both inheriting the same genetic material. The presence of unattached kinetochore at anaphase onset is dangerous, since it may lead to unbalanced ploidy of daughter cells, with severe consequences for their survival. For this reason, improperly attached chromosomes activate the mitotic checkpoint that arrests cell division before anaphase. Cells can maintain an arrest for several hours but eventually will resume proliferation, a process we refer to as adaptation. Whether adapting cells bypass an active block or whether the block has to be removed to resume proliferation is not clear. Likewise, it is not known whether all cells of a genetically homogeneous population are equally capable to adapt. Here, we show that the mitotic checkpoint is operational when yeast cells adapt and that each cell has the same propensity to adapt. Our results are consistent with a model of the mitotic checkpoint where adaptation is driven by random fluctuations of APC/CCdc20 , the molecular species inhibited by the checkpoint. Our data provide a quantitative framework for understanding how cells overcome a constant stimulus that halts cell cycle progression