Shibo Li

Development and evaluation of automated systems for detection and classification of banded chromosomes : current status and future perspectives

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images

Visual search and identification of analyzable metaphase chromosomes using optical microscopes is a very tedious and time-consuming task that is routinely performed in genetic laboratories to detect and diagnose cancers and genetic diseases. The purpose of this study is to develop and test a computerized scheme that can automatically identify chromosomes in metaphase stage and classify them into analyzable and un-analyzable groups. Two independent datasets involving 170 images are used to train and test the scheme. The scheme uses image filtering, threshold, and labeling algorithms to detect chromosomes, followed by computing a set of features for each individual chromosome as well as for each identified metaphase cell. Two machine learning classifiers including a decision tree (DT) based on the features of individual chromosomes and an artificial neural network (ANN) using the features of the metaphase cells are optimized and tested to classify between analyzable and un-analyzable cells. Using the DT based classifier the Kappa coefficients for agreement between the cytogeneticist and the scheme are 0.83 and 0.89 for the training and testing datasets, respectively. We apply an independent testing and a 2-fold cross-validation method to assess the performance of the ANN-based classifier. The area under and receiver operating characteristic (ROC) curve is 0.93 for the complete dataset. This preliminary study demonstrates the feasibility of developing a computerized scheme to automatically identify and classify metaphase chromosomes.

A rule-based computer scheme for centromere identification and polarity assignment of metaphase chromosomes

Automatic centromere identification and polarity assignment are two key factors in the automatic karyotyping of human chromosomes. A multi-stage rule-based computer scheme has been investigated to automatically detect centomeres and determine polarities for both abnormal and normal metaphase chromosomes. The scheme first implements a modified thinning algorithm to identify the medial axis of a chromosome and extracts three feature profiles. Based on a set of pre-optimized classification rules, the scheme adaptively identifies the centromere and then assigns corresponding polarity. An image dataset of 2287 chromosomes acquired from 24 abnormal and 26 normal Giemsa metaphase cells is utilized to optimize and test the scheme. The overall accuracy is 91.4% for centromere identification and 97.4% for polarity assignment. The experimental results demonstrate that our scheme can be successfully applied to diverse chromosomes, which include those severely bent and abnormal chromosomes extracted from cancer cells.

Prenatal Identification of a Novel R937P L1CAM Missense Mutation

The L1 cell adhesion molecule (L1CAM) is a protein encoded by a gene that has been localized to Xq28, is a member of the immunoglobulin superfamily of neuronal cell adhesion molecules, and plays a role in CNS development and maturation. L1CAM is expressed in neurons and Schwann cells, where it is active in neurite overgrowth, adhesion fasciculation, migration, myelination, and axon guidance. Mutations within the gene have been associated with phenotypic changes that include hydrocephalus due to aqueductal stenosis, agenesis or hypoplasia of the corpus callosum and corticospinal tracts, mental retardation, spastic paraplegia, and adducted thumbs. Here, we present a 19-year-old primigravida Caucasian woman who was referred to us in the 27th week of the pregnancy because of fetal polyhydramnios and ventriculomegaly. Our evaluation identified a male fetus with hydrocephalus, ventriculomegaly, aqueductal stenosis, and polyhydramnios. An amniocentesis was performed, and isolated fetal DNA revealed a hemizygous G > C mutation in codon 2809 of exon 21 of the L1CAM gene. The patient was later tested and identified to be a carrier of the same mutation. The fetus was delivered during the 38th week. Neonatal physical examination revealed marked frontal bossing, contractures of the feet with rocker bottom appearance, and hyperactive reflexes with ankle and knee clonus. He died at 4 months of life.

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.

A computer-aided method to expedite the evaluation of prognosis for childhood acute lymphoblastic leukemia

This study presented a fully-automated computer-aided method (scheme) to detect metaphase chromosomes depicted on microscopic digital images, count the total number of chromosomes in each metaphase cell, compute the DNA index, and correlate the results to the prognosis of childhood acute lymphoblastic leukemia (ALL). The computer scheme first uses image filtering, threshold, and labeling algorithms to segment and count the number of the suspicious “chromosome,” and then computes a feature vector for each “detected chromosome.” Based on these features, a knowledge-based classifier is used to eliminate those “non-chromosome” objects (i.e., inter-phase cells, stain debris, and other kinds of background noises). Due to the possible overlap of the chromosomes, a classification criterion was used to identify the overlapped chromosomes and adjust the initially counted number of the total chromosomes in each image. In this preliminary study with 60 testing images (depicting metaphase chromosome cells) acquired from three pediatric patients, the computer scheme generated results matched with the diagnostic results provided by the clinical cytogeneticists. The results demonstrated the feasibility or potential of using a computerized method to replace the tedious and the reader-dependent diagnostic methods commonly used in genetic laboratories to date.

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.

Duplication 15q as the sole anomaly in an acute promyelocytic leukemia patient without t(15;17)

We present a unique chromosomal abnormality found in a patient with acute myeloblastic leukemia of French-American-British subtype M3. The patient was referred for an evaluation of a chromosomal anomaly exclusively associated with FAB M3 or acute promyelocytic leukemia: a translocation between chromosomes 15 and 17, t(15;17)(q22;q21.1). Neither t(15;17) nor rearrangement of RARalpha was detected by routine G-banded chromosome as well as fluorescence in situ hybridization analysis using the commercial dual-color PML/RARalpha translocation probe and the RARalpha probe, a break apart rearrangement dual-color probe. Instead of the typical rearrangement between chromosomes 15 and 17, all cells analyzed had a duplication of the segment of chromosome 15 between bands 15q15 and 15q26.

Feature selection for the automated detection of metaphase chromosomes: Performance comparison using a receiver operating characteristic method

Background. The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented. Methods. In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method. Results. The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity. Conclusion. This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.

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.