Xiaodong Chen

Automated Analysis of Fluorescent in situ Hybridization (FISH) Labeled Genetic Biomarkers in Assisting Cervical Cancer Diagnosis

The numerical and/or structural deviation of some chromosomes (i.e., monosomy and polysomy of chromosomes 3 and X) are routinely used as positive genetic biomarkers to diagnose cervical cancer and predict the disease progression. Among the available diagnostic methods to analyze the aneusomy of chromosomes 3 and X, fluorescence in situ hybridization (FISH) technology has demonstrated significant advantages in assisting clinicians to more accurately detect and diagnose cervical carcinoma at an early stage, in particular for the women at a high risk for progression of low-grade and high-grade squamous intraepithelium lesions (LSIL and HSIL). In order to increase the diagnostic accuracy, consistency, and efficiency from that of manual FISH analysis, this study aims to develop and test an automated FISH analysis method that includes a two-stage scheme. In the first stage, an interactive multiple-threshold algorithm is utilized to segment potential interphase nuclei candidates distributed in different intensity levels and a rule-based classifier is implemented to identify analyzable interphase cells. In the second stage, FISH labeled biomarker spots of chromosomes 3 and X are segmented by a top-hat transform. The independent FISH spots are then detected by a knowledge-based classifier, which enables recognition of the splitting and stringy FISH signals. Finally, the ratio of abnormal interphase cells with numerical changes of chromosomes 3 and X is calculated to detect positive cases. The experimental results of four test cases showed high agreement of FISH analysis results between the automated scheme and the cytogeneticists analysis including 92.7% to 98.7% agreement in cell segmentation and 4.4% to 11.0% difference in cell classification. This preliminary study demonstrates the feasibility of potentially applying the automatic FISH analysis method to expedite the screening and detecting cervical cancer at an early stage.

Optical and Digital Microscopic Imaging Techniques and Applications in Pathology

The conventional optical microscope has been the primary tool in assisting pathological examinations. The modern digital pathology combines the power of microscopy, electronic detection, and computerized analysis. It enables cellular-, molecular-, and genetic-imaging at high efficiency and accuracy to facilitate clinical screening and diagnosis. This paper first reviews the fundamental concepts of microscopic imaging and introduces the technical features and associated clinical applications of optical microscopes, electron microscopes, scanning tunnel microscopes, and fluorescence microscopes. The interface of microscopy with digital image acquisition methods is discussed. The recent developments and future perspectives of contemporary microscopic imaging techniques such as three-dimensional and in vivo imaging are analyzed for their clinical potentials.

Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images

Karyotyping is an important process to classify chromosomes into standard classes and the results are routinely used by the clinicians to diagnose cancers and genetic diseases. However, visual karyotyping using microscopic images is time-consuming and tedious, which reduces the diagnostic efficiency and accuracy. Although many efforts have been made to develop computerized schemes for automated karyotyping, no schemes can get be performed without substantial human intervention. Instead of developing a method to classify all chromosome classes, we develop an automatic scheme to detect abnormal metaphase cells by identifying a specific class of chromosomes (class 22) and prescreen for suspicious chronic myeloid leukemia (CML). The scheme includes three steps: (1) iteratively segment randomly distributed individual chromosomes, (2) process segmented chromosomes and compute image features to identify the candidates, and (3) apply an adaptive matching template to identify chromosomes of class 22. An image data set of 451 metaphase cells extracted from bone marrow specimens of 30 positive and 30 negative cases for CML is selected to test the schemes performance. The overall case-based classification accuracy is 93.3% (100% sensitivity and 86.7% specificity). The results demonstrate the feasibility of applying an automated scheme to detect or prescreen the suspicious cancer cases.

Evaluations of auto-focusing methods under a microscopic imaging modality for metaphase chromosome image analysis

Background: Auto-focusing is an important operation in high throughput imaging scanning. Although many auto-focusing methods have been developed and tested for a variety of imaging modalities, few investigations have been performed on the selection of an optimal auto-focusing method that is suitable for the pathological metaphase chromosome analysis under a high resolution scanning microscopic system. Objective: The purpose of this study is to investigate and identify an optimal auto-focusing method for the pathological metaphase chromosome analysis. Methods: In this study, five auto-focusing methods were applied and tested using metaphase chromosome images acquired from bone marrow and blood specimens. These methods were assessed by measuring a number of indices including execution time, accuracy, number of false maxima, and full width at half maximum (FWHM). Results: For the specific condition investigated in this study, the results showed that the Brenner gradient and threshold pixel counting methods were the optimal methods for acquiring high quality metaphase chromosome images from the bone marrow and blood specimens, respectively. Conclusions: Selecting an optimal auto-focusing method depends on the specific clinical tasks. This study also provides useful information for the design and implementation of the high throughput microscopic image scanning systems in the future digital pathology.

New method for determining the depth of field of microscope systems

This paper presents new formulas to determine the depth of field (DOF) of optical and digital microscope systems. Unlike the conventional DOF formula, the new methods consider the interplay of geometric and diffraction optics for infinite and finite optical microscopes and for corresponding digital microscope systems. It is shown that in addition to the well understood parameters such as numerical apertures, focal length, and light wavelength, system components such as aperture stops also affect the DOF. For the same objective lens, the DOF is inversely proportional to the size of the aperture stop, and it is proportional to the focal length of the ocular lens. It is also shown that under optimal viewing and operating conditions, the visual accommodation of human observers has no meaningful impact on DOF. The new formulas reported are useful for accurately calculating the DOF of microscopes.

Impact of the optical depth of field on cytogenetic image quality

Abstract. In digital pathology, clinical specimen slides are converted into digital images by microscopic image scanners. Since random vibration and mechanical drifting are unavoidable on even high-precision moving stages, the optical depth of field (DOF) of microscopic systems may affect image quality, in particular when using an objective lens with high magnification power. The DOF of a microscopic system was theoretically analyzed and experimentally validated using standard resolution targets under 60× dry and 100× oil objective lenses, respectively. Then cytogenetic samples were imaged at in-focused and off-focused states to analyze the impact of DOF on the acquired image qualities. For the investigated system equipped with the 60× dry and 100× oil objective lenses, the theoretical estimation of the DOF are 0.855 μm and 0.703 μm, and the measured DOF are 3.0 μm and 1.8 μm, respectively. The observation reveals that the chromosomal bands of metaphase cells are distinguishable when images are acquired up to approximately 1.5 μm or 1 μm out of focus using the 60× dry and 100× oil objective lenses, respectively. The results of this investigation provide important designing trade-off parameters to optimize the digital microscopic image scanning systems in the future.

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of applying this CAD-guided high-resolution microscopic image scanning system to prescreen and select ROIs that may contain analyzable metaphase chromosome cells. The success and the further improvement of this automated scanning system may have great impact on the future clinical practice in genetic laboratories to detect and diagnose diseases.

An automatic scanning method for high throughput microscopic system to facilitate medical genetic diagnosis: an initial study

The purpose of this paper is to report a new automatic scanning scheme for high throughput microscopic systems aiming to facilitate disease diagnosis in genetic laboratories. To minimize the impact of the random vibration and mechanical drifting of the scanning stage in microscopic image acquisition, auto-focusing operations are usually applied repeatedly during the scanning process. Such methods ensure the acquisition of well focused images for clinical diagnosis, but are time consuming. The technique investigated in this preliminary study applies the auto-focusing operations at a limited number of locations on the slide. For the rest of the imaging field, the focusing position is quickly adjusted through linear interpolation. In this initial validation study, blood pathological slides containing both metaphase and interphase cells are scanned. For a selected area of 6.9mm×6.9mm, a number of 2×2, 3×2, 3×3, and 4×4 positions are evenly sampled for auto-focusing operations. Respectively, 25, 29, 40, and 41 clinically meaningful cells are identified for each sampling scheme. For the specific case investigated, the results demonstrate that the 4 position auto-focusing scheme could obtain the adequate number of clinically meaningful cells for the diagnosis. The schemes with more auto-focusing operations provide an option for high reliability diagnosis when clinically necessary. More comprehensive research is planned, and that may lead to optimal design of trade-off for developing the scanning scheme of the high throughput microscopic systems.

The impact of the condenser on cytogenetic image quality in digital microscope system

Background: Optimizing operational parameters of the digital microscope system is an important technique to acquire high quality cytogenetic images and facilitate the process of karyotyping so that the efficiency and accuracy of diagnosis can be improved. OBJECTIVE: This study investigated the impact of the condenser on cytogenetic image quality and system working performance using a prototype digital microscope image scanning system. Methods: Both theoretical analysis and experimental validations through objectively evaluating a resolution test chart and subjectively observing large numbers of specimen were conducted. Results: The results show that the optimal image quality and large depth of field (DOF) are simultaneously obtained when the numerical aperture of condenser is set as 60%–70% of the corresponding objective. Under this condition, more analyzable chromosomes and diagnostic information are obtained. As a result, the system shows higher working stability and less restriction for the implementation of algorithms such as autofocusing especially when the system is designed to achieve high throughput continuous image scanning. Conclusions: Although the above quantitative results were obtained using a specific prototype system under the experimental conditions reported in this paper, the presented evaluation methodologies can provide valuable guidelines for optimizing operational parameters in cytogenetic imaging using the high throughput continuous scanning microscopes in clinical practice.

Fluorescence in situ hybridization (FISH) signal analysis using automated generated projection images

Fluorescence in situ hybridization (FISH) tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD) schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D) image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.