Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections

A fast, fully automated cell segmentation algorithm for high-throughput and high-content screening

High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application.

A confident scale-space shape representation framework for cell migration detection

Automated segmentation of time-lapse images is a method to facilitate the understanding of the intricate biological progression, e.g. cancer cell migration. To address this problem, we introduce a shape representation enhancement over popular snake models in the context of confident scale-space such that a higher level of interpretation can hopefully be achieved. Our proposed system consists of a hierarchical analytic framework including feedback loops, self-adaptive and demand-adaptive adjustment, incorporating a steerable boundary detail term constraint based on multiscale B-spline interpolation. To minimize the noise interference inherited from microscopy acquisition, the coarse boundary derived from the initial segmentation with refined watershed line is coupled with microscopy compensation using the mean shift filtering. A progressive approximation is applied to achieve represented as a balance between a relief function of watershed algorithm and local minima concerning multiscale optimality, convergence and robust constraints. Experimental results show that the proposed method overcomes problems with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio. The proposed system has the potential to serve as an automated data processing tool for cell migration applications.

Segmentation of touching cell nuclei using gradient flow tracking

Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.

Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

A high-throughput system for segmenting nuclei using multiscale techniques

Automatic segmentation of cell nuclei is critical in several high-throughput cytometry applications whereas manual segmentation is laborious and irreproducible. One such emerging application is measuring the spatial organization (radial and relative distances) of fluorescence in situ hybridization (FISH) DNA sequences, where recent investigations strongly suggest a correlation between nonrandom arrangement of genes to carcinogenesis. Current automatic segmentation methods have varying performance in the presence of nonuniform illumination and clustering, and boundary accuracy is seldom assessed, which makes them suboptimal for this application. The authors propose a modular and model-based algorithm for extracting individual nuclei. It uses multiscale edge reconstruction for contrast stretching and edge enhancement as well as a multiscale entropy-based thresholding for handling nonuniform intensity variations. Nuclei are initially oversegmented and then merged based on area followed by automatic multistage classification into single nuclei and clustered nuclei. Estimation of input parameters and training of the classifiers is automatic. The algorithm was tested on 4,181 lymphoblast nuclei with varying degree of background nonuniformity and clustering. It extracted 3,515 individual nuclei and identified single nuclei and individual nuclei in clusters with 99.8 +/- 0.3% and 95.5 +/- 5.1% accuracy, respectively. Segmented boundaries of the individual nuclei were accurate when compared with manual segmentation with an average RMS deviation of 0.26 microm (approximately 2 pixels). The proposed segmentation method is efficient, robust, and accurate for segmenting individual nuclei from fluorescence images containing clustered and isolated nuclei. The algorithm allows complete automation and facilitates reproducible and unbiased spatial analysis of DNA sequences.

High throughput microscopy: from raw images to discoveries

Technological advances in automated microscopy now allow rapid acquisition of many images without human intervention, images that can be used for large-scale screens. The main challenge in such screens is the conversion of the raw images into interpretable information and hence discoveries. This post-acquisition component of image-based screens requires computational steps to identify cells, choose the cells of interest, assess their phenotype, and identify statistically significant 'hits'. Designing such an analysis pipeline requires careful consideration of the necessary hardware and software components, image analysis, statistical analysis and data presentation tools. Given the increasing availability of such hardware and software, these types of experiments have come within the reach of individual labs, heralding many interesting new ways of acquiring biological knowledge.

An automated method for cell detection in zebrafish

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

Cylindrical projection of electrostatic potential and image analysis tools for damaged DNA: the substitution of thymine with thymine glycol

Changes of electrostatic potential around the DNA molecule resulting from chemical modifications of nucleotides may play a role in enzymatic recognition of damaged sites. Effects of chemical modifications of nucleotides on the structure of DNA have been characterized through electronic structure computations. Quantum mechanical structural optimizations of fragments of five pairs of nucleotides with thymine or thymine glycol were performed at the density functional level of theory with a B3LYP exchange-correlation functional and 6-31G(d,p) basis sets. The electrostatic potential (EP) around DNA fragments was projected on a cylindrical surface around the double helix. The 2D maps of EP of intact and damaged DNA fragments were compared using image analysis methods to identify and measure modifications of the EP that result from the occurrence of thymine glycol. It was found that distortions of phosphate groups and displacements of the accompanying countercations by up to approximately 0.5 angstroms along the axis of DNA are clearly reflected in the EP maps. Modifications of the EP in the major groove of DNA near the damaged site are also reported.

Automatic segmentation of high-throughput RNAi fluorescent cellular images

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.

A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.

3D cell nuclei segmentation based on gradient flow tracking

Background

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.

Results

Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.

Conclusion

The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.

CellProfiler: free, versatile software for automated biological image analysis

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.

SEMANTIC ANALYSIS OF BIOLOGICAL IMAGING DATA: CHALLENGES AND OPPORTUNITIES

Microscopic imaging is one of the most common techniques for investigating biological systems. In recent years there has been a tremendous growth in the volume of biological imaging data owing to rapid advances in optical instrumentation, high-speed cameras and fluorescent probes. Powerful semantic analysis tools are required to exploit the full potential of the information content of these data. Semantic analysis of multi-modality imaging data, however, poses unique challenges. In this paper we outline the state-of-the-art in this area along with the challenges facing this domain. Information extraction from biological imaging data requires modeling at multiple levels of detail. While some applications require only quantitative analysis at the level of cells and subcellular objects, others require modeling of spatial and temporal changes associated with dynamic biological processes. Modeling of biological data at different levels of detail allows not only quantitative analysis but also the extraction of high-level semantics. Development of powerful image interpretation and semantic analysis tools has the potential to significantly help in understanding biological processes, which in turn will result in improvements in drug development and healthcare.

Combination of quantification and observation methods for study of “Side Population” cells in their “in vitro” microenvironment

BACKGROUND

Qualitative and quantitative analyses of the rare phenotypic variants in in vitro culture systems is necessary for the understanding of cell differentiation in cell culture of primary cells or cell lines. Slide-based cytometry combines image acquisition and data treatment, and associates the power of flow cytometry (FCM) and the resolution of the microscopic studies making it suitable for the analysis of cells with rare phenotype. In this paper we develop a method that applies these principles to a particularly hot problem in cell biology, the study of stem cell like cells in cultures of primary cells, cancer cells, and various cell lines.

METHODS

The adherent cells were labeled by the fluorescent dye Hoechst 33342. The images of cell populations were collected by a two-photon microscope and processed by a software developed by us. The software allows the automated segmentation of the nuclei in a very dense cell environment, the measurement of the fluorescence intensity of each nucleus and the recording of their position in the plate. The cells with a given fluorescence intensity can then be located easily on the recorded image of the culture plate for further analysis.

RESULTS

The potential of our method is illustrated by the identification and localization of SP cells in the cultures of the C2C12 cell line. Although these cells represent only about 1% of the total population as calculated by flow cytometry, they can be identified in the culture plate with high precision by microscopy.

CONCLUSION

Cells with the rare stem-cell like phenotype can be efficiently identified in the undisturbed cultures. Since the fluorescence intensity of rare events and the position of thousands of surrounding cells are recorded at the same time, the method associates the advantage of the FCM analysis and the microscopic observation.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

Supervised learning-based cell image segmentation for p53 immunohistochemistry

In this paper, we present two new algorithms for cell image segmentation. First, we demonstrate that pixel classification-based color image segmentation in color space is equivalent to performing segmentation on grayscale image through thresholding. Based on this result, we develop a supervised learning-based two-step procedure for color cell image segmentation, where color image is first mapped to grayscale via a transform learned through supervised learning, thresholding is then performed on the grayscale image to segment objects out of background. Experimental results show that the supervised learning-based two-step procedure achieved a boundary disagreement (mean absolute distance) of 0.85 while the disagreement produced by the pixel classification-based color image segmentation method is 3.59. Second, we develop a new marker detection algorithm for watershed-based separation of overlapping or touching cells. The merit of the new algorithm is that it employs both photometric and shape information and combines the two naturally in the framework of pattern classification to provide more reliable markers. Extensive experiments show that the new marker detection algorithm achieved 0.4% and 0.2% over-segmentation and under-segmentation, respectively, while reconstruction-based method produced 4.4% and 1.1% over-segmentation and under-segmentation, respectively.

Methods for compensation of the light attenuation with depth of images captured by a confocal microscope

A confocal laser scanning microscope (CLSM) enables us to capture images from a biological specimen in different depths and obtain a series of precisely registered fluorescent images. However, images captured from deep layers of the specimen may be darker than images from the topmost layers because of light loss distortions. This effect causes difficulties in subsequent analysis of biological objects. We propose a solution using two approaches: either an online method working already during image acquisition or an offline method assisting as a postprocessing step. In the online method, the gain value of a photomultiplier tube of a CLSM is controlled according to the difference of mean image intensities between the reference and currently acquired image. The offline method consists of two stages. In the first stage, a standard histogram maintaining relative frequencies of gray levels and improving brightness and contrast is created from all images in the series. In the second stage, individual image histograms are warped according to this standard histogram. The methods were tested on real confocal image data captured from human placenta and rat skeletal muscle specimens. It was shown that both approaches diminish the light attenuation in images captured from deep layers of the specimen.

Automated criteria-based selection and analysis of fluorescent synaptic puncta

The use of fluorescent probes such as FM 1-43 or synapto-pHluorin to study the dynamic aspects of synaptic function has dramatically increased in recent years. The analysis of such experiments is both labor intensive and subject to potentially significant experimenter bias. For our analysis of fluorescently labeled synapses in cultured hippocampal neurons, we have developed an automated approach to punctum identification and classification. This automatic selection and processing of fluorescently labeled synaptic puncta not only reduces the chance of subjective bias and improves the quality and reproducibility of the analyses, but also greatly increases the number of release sites that can be rapidly analyzed from a given experiment, increasing the signal-to-noise ratio of the data. An important innovation to the automation of analysis is our method for objectively selecting puncta for analysis, particularly important for studying and comparing dynamic functional properties of a large population of individual synapses. The fluorescence change for each individual punctum is automatically scored according to several criteria, allowing objective assessment of the quality of each site. An entirely automated and thus unbiased analysis of fluorescence in the study of synaptic function is critical to providing a comprehensive understanding of the cellular and molecular underpinnings of neurotransmission and plasticity.

Whole cell segmentation in solid tissue sections

BACKGROUND

Understanding the cellular and molecular basis of tissue development and function requires analysis of individual cells while in their tissue context.

METHODS

We developed software to find the optimum border around each cell (segmentation) from two-dimensional microscopic images of intact tissue. Samples were labeled with a fluorescent cell surface marker so that cell borders were brighter than elsewhere. The optimum border around each cell was defined as the border with an average intensity per unit length greater that any other possible border around that cell, and was calculated using the gray-weighted distance transform. Algorithm initiation requiring the user to mark two points per cell, one approximately in the center and the other on the border, ensured virtually 100% correct segmentation. Thereafter segmentation was automatic.

RESULTS

The method was highly robust, because intermittent labeling of the cell borders, diffuse borders, and spurious signals away from the border do not significantly affect the optimum path. Computer-generated cells with increasing levels of added noise showed that the approach was accurate provided the cell could be detected visually.

CONCLUSIONS

We have developed a highly robust algorithm for segmenting images of surface-labeled cells, enabling accurate and quantitative analysis of individual cells in tissue.

Quantitative image analysis in mammary gland biology

In this paper we present a summary of recent quantitative approaches used for the analysis of macro and microscopic images in mammary gland biology. The advantages and disadvantages of whole mount analysis, reconstruction of serial tissue sections and nucleus/cell segmentation of either conventional and confocal images are discussed, as are applications of quantitative image analysis, such as quantification of protein levels or vasculature measurements in normal tissue and cancer. Integration of quantitative imaging into the further study of the mammary gland holds the promise of better understanding its tissue complexity that evolves during development, differentiation and disease.