Applications of quantitative digital image analysis to breast cancer research

Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes

Background

Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance.

Results

We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in oocyte appearance in the nematode Caenorhabditis elegans. Max–min value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max–min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max–min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging.

Conclusions

The Max–min value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.

Remote imaging of single cell 3D morphology with ultrafast coherent phonons and their resonance harmonics

Cell morphological analysis has long been used in cell biology and physiology for abnormality identification, early cancer detection, and dynamic change analysis under specific environmental stresses. This work reports on the remote mapping of cell 3D morphology with an in-plane resolution limited by optics and an out-of-plane accuracy down to a tenth of the optical wavelength. For this, GHz coherent acoustic phonons and their resonance harmonics were tracked by means of an ultrafast opto-acoustic technique. After illustrating the measurement accuracy with cell-mimetic polymer films we map the 3D morphology of an entire osteosarcoma cell. The resulting image complies with the image obtained by standard atomic force microscopy, and both reveal very close roughness mean values. In addition, while scanning macrophages and monocytes, we demonstrate an enhanced contrast of thickness mapping by taking advantage of the detection of high-frequency resonance harmonics. Illustrations are given with the remote quantitative imaging of the nucleus thickness gradient of migrating monocyte cells.

Hormonally responsive breast cancer cells in a microfluidic co-culture model as a sensor of microenvironmental activity

Breast cancer cell growth and therapeutic response are manipulated extrinsically by microenvironment signals. Despite recognition of the importance of the microenvironment in a variety of tumor processes, predictive measures that incorporate the activity of the surrounding cellular environment are lacking. In contrast, tumor cell biomarkers are well established in the clinic. Expression of Estrogen Receptor-alpha (ERα) is the primary defining feature of hormonally responsive tumors and is the molecular target of therapy in the most commonly diagnosed molecular subtype of breast cancer. While a number of soluble factors have been implicated in ERα activation, the complexity of signaling between the cellular microenvironment and the cancer cell implies multivariate control. The cumulative impact of the microenvironment signaling, which we define as microenvironmental activity, is more difficult to predict than the sum of its parts. Here we tested the impact of an array of microenvironments on ERα signaling utilizing a microfluidic co-culture model. Quantitative immunofluorescence was employed to assess changes in ERα protein levels, combined with gene expression and phosphorylation status, as measures of activation. Analysis of microenvironment-induced growth under the same conditions revealed a previously undescribed correlation between growth and ERα protein down-regulation. These data suggest an expanded utility for the tumor biomarker ERα, in which the combination of dynamic regulation of ERα protein and growth in a breast cancer biosensor cell become a read-out of the microenvironmental activity.

Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features

Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best classification performance.

Applications of 'TissueQuant'- a color intensity quantification tool for medical research

This paper demonstrates the use of TissueQuant - an image analysis tool for quantification of color intensities which was developed for use in medical research where the stained biological specimen such as tissue or antigen needs to be quantified. TissueQuant provides facilities for user interaction to choose and quantify the color of interest and its shades. Gaussian weighting functions are used to provide a color score which quantifies how close the shade is to the user specified reference color. We describe two studies in medical research which use TissueQuant for quantification. The first study evaluated the effect of petroleum-ether extract of Cissus quadrangularis (CQ) on osteoporotic rats. It was found that the analysis results correlated well with the manual evaluation, p < 0.001. The second study evaluated the nerve morphometry and it was found that the adipose and non adipose tissue content was maximum in radial nerve among the five nerves studied.

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Early detection in head and neck cancer - current state and future perspectives

Survival and quality of life in head and neck cancer are directly linked to the size of the primary tumor at first detection. In order to achieve substantial gain at these issues, both, primary prevention and secondary prevention, which is early detection of malignant lesions at a small size, have to be improved. So far, there is not only a lack in the necessary infrastructure not only in Germany, but rather worldwide, but additionally the techniques developed so far for early detection have a significance and specificity too low as to warrant safe implementation for screening programs. However, the advancements recently achieved in endoscopy and in quantitative analysis of hypocellular specimens open new perspectives for secondary prevention. Chromoendoscopy and narrow band imaging (NBI) pinpoint suspicious lesions more easily, confocal endomicroscopy and optical coherence tomography obtain optical sections through those lesions, and hyperspectral imaging classifies lesions according to characteristic spectral signatures. These techniques therefore obtain optical biopsies. Once a "bloody" biopsy has been taken, the plethora of parameters that can be quantified objectively has been increased and could be the basis for an objective and quantitative classification of epithelial lesions (multiparametric cytometry, quantitative histology). Finally, cytomics and proteomics approaches, and lab-on-the-chip technology might help to identify patients at high-risk. Sensitivity and specificity of these approaches have to be validated, yet, and some techniques have to be adapted for the specific conditions for early detection of head and neck cancer. On this background it has to be stated that it is still a long way to go until a population based screening for head and neck cancer is available. The recent results of screening for cancer of the prostate and breast highlight the difficulties implemented in such a task.

Analyzing and mining image databases

Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications, in particular automated imaging at the cellular level. An image database is an interactive software application that combines data management, image analysis and visual data mining. The main characteristic of such a system is a layer that represents objects within an image, and that represents a large spectrum of quantitative and semantic object features. The image analysis needs to be adapted to each particular experiment, so 'end-user programming' will be desirable to make the technology more widely applicable.

The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy

The creation of structured shared data repositories for molecular data in the form of web-accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web-accessible database, the Cell Centered Database (CCDB; http://ncmir.ucsd.edu/CCDB/), designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.

From cells to tissues: Fluorescence confocal microscopy in the study of histological samples

Our knowledge of the genetic mechanisms controlling cell proliferation and differentiation usually originates from in vitro cultured cell line models. However, the definition of the molecular switches involved in control of homeostasis and the understanding of the changes occurring in neoplastic transformation require looking at single cells as the components of a complex tissue network. Histological examination of tissue samples can gain a substantial amount of information from high-resolution fluorescence analysis. In particular, confocal microscopy can help in the definition of functional pathways using multiparameter analysis. In this report, we present acquisition and analysis procedures to obtain high-resolution data from tissue sections. Confocal microscopy coupled to computational restoration, statistical evaluation of spatial correlations, and morphological analysis over large tissue areas were applied to colorectal samples providing a molecular fingerprint of the biological differences inferred from classical histological examination.