A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

Accelerating x-ray data collection using pyramid beam ray casting geometries

Image reconstruction from its projections is a necessity in many applications such as medical (CT), security, inspection, and others. This paper extends the 2-D Fan-beam method in [2] to 3-D. The algorithm, called Pyramid Beam (PB), is based upon the parallel reconstruction algorithm in [1]. It allows fast capturing of the scanned data, and in 3-D, the reconstructions are based upon the discrete X-ray transform [1]. The PB geometries are reordered to fit parallel projection geometry. The underlying idea is to use the algorithm in [1] by porting the proposed PB geometries to fit the algorithm in [1]. The complexity of the algorithm is comparable with the 3-D FFT. The results show excellent reconstruction qualities while being simple for practical use.

Supervised learning framework for screening nuclei in tissue sections

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D

BACKGROUND

To exploit the flood of data from advances in high throughput imaging of optically sectioned nuclei, image analysis methods need to correctly detect thousands of nuclei, ideally in real time. Variability in nuclear appearance and undersampled volumetric data make this a challenge.

RESULTS

We present a novel 3D nuclear identification method, which subdivides the problem, first segmenting nuclear slices within each 2D image plane, then using a shape model to assemble these slices into 3D nuclei. This hybrid 2D/3D approach allows accurate accounting for nuclear shape but exploits the clear 2D nuclear boundaries that are present in sectional slices to avoid the computational burden of fitting a complex shape model to volume data. When tested over C. elegans, Drosophila, zebrafish and mouse data, our method yielded 0 to 3.7% error, up to six times more accurate as well as being 30 times faster than published performances. We demonstrate our method's potential by reconstructing the morphogenesis of the C. elegans pharynx. This is an important and much studied developmental process that could not previously be followed at this single cell level of detail.

CONCLUSIONS

Because our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches. Both of these characteristics are necessary for practical analysis of overwhelmingly large data sets where processing must be scalable to hundreds of thousands of cells and where the time cost of manual error correction makes it impossible to use data with high error rates. Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain. As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing

Motivation: Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them.

Methods: We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location.

Results: We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy.

Availability:http://murphylab.web.cmu.edu/software

Contact:murphy@cmu.edu

Tracking and quantitative analysis of dynamic movements of cells and particles

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.

Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models

Three-dimensional (2D) cell culture models have emerged as the basis for improved cell systems biology. However, there is a gap in robust computational techniques for segmentation of these model systems that are imaged through confocal or deconvolution microscopy. The main issues are the volume of data, overlapping subcellular compartments and variation in scale or size of subcompartments of interest, which lead to ambiguities for quantitative analysis on a cell-by-cell basis. We address these ambiguities through a series of geometric operations that constrain the problem through iterative voting and decomposition strategies. The main contributions of this paper are to (i) extend the previously developed 2D radial voting to an efficient 3D implementation, (ii) demonstrate application of iterative radial voting at multiple subcellular and molecular scales, and (iii) investigate application of the proposed technology to two endpoints between 2D and 3D cell culture models. These endpoints correspond to kinetics of DNA damage repair as measured by phosphorylation of γH2AX, and the loss of the membrane-bound E-cadherin protein as a result of ionizing radiation. Preliminary results indicate little difference in the kinetics of the DNA damage protein between 2D and 3D cell culture models; however, differences between membrane-bound E-cadherin are more pronounced.

Population estimation of human embryonic stem cell cultures

Traditionally, the population of human embryonic stem cell (hESC) culture is estimated through haemacytometer counts, which include harvesting the cells and manually analyzing a fraction of an entire population. Obviously, through this highly invasive method, it is not possible to preserve any spatial information on the cell population. The goal of this study is to identify a fast and consistent method for in situ automated hESC population estimation to quantitatively estimate the cell growth. Therefore, cell cultures were fixed, stained, and their nuclei imaged through high-resolution microscopy, and the images were processed with different image analysis techniques. The proposed method first identifies signal and background by computing an image specific threshold for image segmentation. By applying a morphological operator (watershed), we split most physically overlapping nuclei, leading to a pixel area distribution of isolated signal areas on the image. On the basis of this distribution, we derive a nucleus area model, describing the distribution of the area of cell debris, single nuclei, and small groups of connected nuclei. Through the model, we can give a quantitative estimation of the population. The focus of this study is on low-density human embryonic stem cell populations; hence cultures were measured at days 2-3 after seeding. Compared with manual cell counts, the automatic method achieved higher accuracy with <6% error.

DeadEasy neurons: automatic counting of HB9 neuronal nuclei in Drosophila

Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities.

A computational framework for studying neuron morphology from in vitro high content neuron-based screening

High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.

Automated image analysis for high-content screening and analysis

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

DeadEasy Mito-Glia: automatic counting of mitotic cells and glial cells in Drosophila

Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists.

Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment

Prevention and early detection of breast cancer are the major prophylactic measures taken to reduce the breast cancer related mortality and morbidity. Clinical management of breast cancer largely relies on the efficacy of the breast-conserving surgeries and the subsequent radiation therapy. A key problem that limits the success of these surgeries is the lack of accurate, real-time knowledge about the positive tumor margins in the surgically excised tumors in the operating room. This leads to tumor recurrence and, hence, the need for repeated surgeries. Current intraoperative techniques such as frozen section pathology or touch imprint cytology severely suffer from poor sampling and non-optimal detection sensitivity. Even though histopathology analysis can provide information on positive tumor margins post-operatively (~2-3 days), this information is of no immediate utility in the operating rooms. In this article, we propose a novel image analysis method for tumor margin assessment based on nuclear morphometry and tissue topology and demonstrate its high sensitivity/specificity in preclinical animal model of breast carcinoma. The method relies on imaging nuclear-specific fluorescence in the excised surgical specimen and on extracting nuclear morphometric parameters (size, number, and area fraction) from the spatial distribution of the observed fluorescence in the tissue. We also report the utility of tissue topology in tumor margin assessment by measuring the fractal dimension in the same set of images. By a systematic analysis of multiple breast tissues specimens, we show here that the proposed method is not only accurate (~97% sensitivity and 96% specificity) in thin sections, but also in three-dimensional (3D) thick tissues that mimic the realistic lumpectomy specimens. Our data clearly precludes the utility of nuclear size as a reliable diagnostic criterion for tumor margin assessment. On the other hand, nuclear area fraction addresses this issue very effectively since it is a combination of both nuclear size and count in any given region of the analyzed image, and thus yields high sensitivity and specificity (~97%) in tumor detection. This is further substantiated by an independent parameter, fractal dimension, based on the tissue topology. Although the basic definition of cancer as an uncontrolled cell growth entails a high nuclear density in tumor regions, a simple but systematic exploration of nuclear distribution in thick tissues by nuclear morphometry and tissue topology as performed in this study has never been carried out, to the best of our knowledge. We discuss the practical aspects of implementing this imaging approach in automated tissue sampling scenario where the accuracy of tumor margin assessment can be significantly increased by scanning the entire surgical specimen rather than sampling only a few sections as in current histopathology analysis.

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

BACKGROUND

Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.

RESULTS

In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://starrynite.sourceforge.net.

CONCLUSIONS

We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.

Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed

Breast tumor volume measured on MRI has been used to assess response to neoadjuvant chemotherapy. However, accurate and reproducible delineation of breast lesions can be challenging, since the lesions may have complicated topological structures and heterogeneous intensity distributions. In this article, the authors present an advanced computerized method to semiautomatically segment tumor volumes on T1-weighted, contrast-enhanced breast MRI. The method starts with manual selection of a region of interest (ROI) that contains the lesion to be segmented in a single image, followed by automated separation of the lesion volume from its surrounding breast parenchyma by using a unique combination of the image processing techniques including Gaussian mixture modeling and a marker-controlled watershed transform. Explicitly, the Gaussian mixture modeling is applied to an intensity histogram of the pixels inside the ROI to distinguish the tumor class from other tissues. Based on the ROI and the intensity distribution of the tumor, internal and external markers are determined and the tumor contour is delineated using the marker-controlled watershed transform. To obtain the tumor volume, the segmented tumor in one slice is propagated to the adjacent slice to form an ROI in that slice. The marker-controlled watershed segmentation is then used again to obtain a tumor contour in the propagated slice. This procedure is terminated when there is no lesion in an adjacent slice. To reduce measurement variations possibly caused by the manual selection of the ROI, the segmentation result is refined based on an automatically determined ROI based on the segmented volume. The algorithm was applied to 13 patients with breast cancer, prospectively accrued prior to beginning neoadjuvant chemotherapy. Each patient had two MRI scans, a baseline MRI examination prior to commencing neoadjuvant chemotherapy and a 1 week follow-up after receiving the first dose of neoadjuvant chemotherapy. Blinded to the computer segmentation results, two experienced radiologists manually delineated all tumors independently. The computer results were then compared with the manually generated results using the volume overlap ratio, defined as the intersection of the computer- and radiologist-generated tumor volumes divided by the union of the two. The algorithm reached overall overlap ratios of 62.6% +/- 9.1% and 61.0% +/- 11.3% in comparison to the two manual segmentation results, respectively. The overall overlap ratio between the two radiologists' manual segmentations was 64.3% +/- 10.4%. Preliminary results suggest that the proposed algorithm is a promising method for assisting in tumor volume measurement in contrast-enhanced breast MRI.

Improved automatic detection and segmentation of cell nuclei in histopathology images

Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

Algorithms for the automated analysis of cellular dynamics within living fungal colonies

We present robust and efficient algorithms to automate the measurement of nuclear movement and germ tube extension rates in living fungal networks. The aim is to facilitate the understanding of the dynamics and regulation of nuclear migration in growing fungal colonies. The proposed methodology combines a cascade correlation filter to identify nuclear centers from which 2D nuclear velocities are determined and a level set algorithm for centerline extraction to monitor spore (conidial) germling growth. We show how the proposed cascaded filter improves spatial resolution in the presence of noise and is robust when fluorescently labeled nuclei with different intensities are in close proximity to each other. The performance of the filter is evaluated by simulation in comparison to the well known Rayleigh and Sparrow criteria, and experimental evidence is given from clusters of nuclei and nuclei undergoing mitotic division. The capabilities developed have enabled the robust and objective analysis of 10's of Gigabytes of image data that is being exploited by biological scientists.

Overlapping nuclei segmentation based on Bayesian networks and stepwise merging strategy

This paper presents a new approach to the segmentation of fluorescence in situ hybridization images. First, to segment the cell nuclei from the background, a threshold is estimated using a Gaussian mixture model and maximizing the likelihood function of the grey values for the cell images. After the nuclei segmentation, the overlapping and isolated nuclei are classified to facilitate a more accurate nuclei analysis. To do this, the morphological features of the nuclei, such their compactness, smoothness and moments, are extracted from training data to generate three probability distribution functions that are then applied to a Bayesian network as evidence. Following the nuclei classification, the overlapping nuclei are segmented into isolated nuclei using an intensity gradient transform and watershed algorithm. A new stepwise merging strategy is also proposed to merge fragments into a major nucleus. Experimental results using fluorescence in situ hybridization images confirm that the proposed system produced better segmentation results when compared to previous methods, because of the nuclei classification before separating the overlapping nuclei.

Automated quantification of DNA demethylation effects in cells via 3D mapping of nuclear signatures and population homogeneity assessment

Today's advanced microscopic imaging applies to the preclinical stages of drug discovery that employ high-throughput and high-content three-dimensional (3D) analysis of cells to more efficiently screen candidate compounds. Drug efficacy can be assessed by measuring response homogeneity to treatment within a cell population. In this study, topologically quantified nuclear patterns of methylated cytosine and global nuclear DNA are utilized as signatures of cellular response to the treatment of cultured cells with the demethylating anti-cancer agents: 5-azacytidine (5-AZA) and octreotide (OCT). Mouse pituitary folliculostellate TtT-GF cells treated with 5-AZA and OCT for 48 hours, and untreated populations, were studied by immunofluorescence with a specific antibody against 5-methylcytosine (MeC), and 4,6-diamidino-2-phenylindole (DAPI) for delineation of methylated sites and global DNA in nuclei (n = 163). Cell images were processed utilizing an automated 3D analysis software that we developed by combining seeded watershed segmentation to extract nuclear shells with measurements of Kullback-Leibler's (K-L) divergence to analyze cell population homogeneity in the relative nuclear distribution patterns of MeC versus DAPI stained sites. Each cell was assigned to one of the four classes: similar, likely similar, unlikely similar, and dissimilar. Evaluation of the different cell groups revealed a significantly higher number of cells with similar or likely similar MeC/DAPI patterns among untreated cells (approximately 100%), 5-AZA-treated cells (90%), and a lower degree of same type of cells (64%) in the OCT-treated population. The latter group contained (28%) of unlikely similar or dissimilar (7%) cells. Our approach was successful in the assessment of cellular behavior relevant to the biological impact of the applied drugs, i.e., the reorganization of MeC/DAPI distribution by demethylation. In a comparison with other metrics, K-L divergence has proven to be a more valuable and robust tool for categorization of individual cells within a population, with potential applications in epigenetic drug screening.

An automatic integrated approach for stained neuron detection in studying neuron migration

Neurons that come to populate the six-layered cerebral cortex are born deep within the developing brain in the surface of the embryonic cerebral ventricles. It is very important to detect these neurons for studying histogenesis of the brain and abnormal migration that had been linked to cognitive deficits, mental retardation, and motor disorders. The visualization of labeled cells in brain sections was performed by immunocytochemical examination and its image data were documented to microscopic pictures. Based on the fact, automatic accurate neurons labeling is prerequisite instead of time-consuming manual labeling. In this article, a fully automated image processing approach is proposed to detect all the stained neurons in microscopic images. First of all, dark stained neurons are achieved by thresholding in blue channel of image. And then a modified fuzzy c-means clustering method, called alternative fuzzy c-means is applied to achieve higher classification accuracy in extracting constraint factor. Finally, watershed based on gradient vector flow is employed to the constraint factor image to segment all the neurons, including clustered neurons. The results demonstrate that the proposed method can be a useful tool in neuron image analysis.

A novel cell segmentation method and cell phase identification using Markov model

Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

A detailed analysis of 3D subcellular signal localization

Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point-like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker-controlled (seeded) 3D watershed segmentation. User-defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point-like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2-Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation.

NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible.The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.

DeadEasy caspase: automatic counting of apoptotic cells in Drosophila

Development, cancer, neurodegenerative and demyelinating diseases, injury, and stem cell manipulations are characterised by alterations in cell number. Research into development, disease, and the effects of drugs require cell number counts. These are generally indirect estimates, because counting cells in an animal or organ is paradoxically difficult, as well as being tedious and unmanageable. Drosophila is a powerful model organism used to investigate the genetic bases of development and disease. There are Drosophila models for multiple neurodegenerative diseases, characterised by an increase in cell death. However, a fast, reliable, and accurate way to count the number of dying cells in vivo is not available. Here, we present a method based on image filtering and mathematical morphology techniques, to count automatically the number of dying cells in intact fruit-fly embryos. We call the resulting programme DeadEasy Caspase. It has been validated for Drosophila and we present examples of its power to address biological questions. Quantification is automatic, accurate, objective, and very fast. DeadEasy Caspase will be freely available as an ImageJ plug-in, and it can be modified for use in other sample types. It is of interest to the Drosophila and wider biomedical communities. DeadEasy Caspase is a powerful tool for the analysis of cell survival and cell death in development and in disease, such as neurodegenerative diseases and ageing. Combined with the power of Drosophila genetics, DeadEasy expands the tools that enable the use of Drosophila to analyse gene function, model disease and test drugs in the intact nervous system and whole animal.

A unified framework for automated 3-d segmentation of surface-stained living cells and a comprehensive segmentation evaluation

This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.

Rapid activation of plasticity-associated gene transcription in hippocampal neurons provides a mechanism for encoding of one-trial experience

The hippocampus is hypothesized to support rapid encoding of ongoing experience. A critical prerequisite for such function is the ability to readily recruit enduring synaptic plasticity in hippocampal neurons. Hippocampal long-term potentiation (LTP) and memory consolidation require expression of the immediate-early gene (IEG) Arc. To determine whether Arc transcription could be driven by limited and controlled behavioral experience, we used a rectangular track paradigm. In past electrophysiological studies, pyramidal neurons recorded from rats running in one direction on similar tracks typically exhibited a single firing field. Using fluorescence in situ hybridization, we show that the behavioral activity associated with a single lap around the track was sufficient to trigger Arc transcription in complete CA3 neuronal ensembles, as predicted given the role of CA3 in one-trial learning. In contrast, Arc transcription in CA1 ensembles was recruited incrementally, with maximal activation achieved after four laps a day for 4 consecutive days. To test whether Arc transcription is linked to learning and plasticity, or merely elicited by location-specific firing, we inactivated the medial septum, a treatment that compromises hippocampus-dependent learning and LTP but spares location-specific firing in CA1 neurons. Septal inactivation abolished track training-induced Arc transcription in CA1 and CA3 neurons, showing that Arc transcription requires plasticity-inducing stimuli. Accordingly, LTP induction activated Arc transcription in CA1 neurons in vivo. These findings demonstrate for the first time that a single brief experience, equivalent to a single crossing of a firing field, can trigger IEG expression required for long-term plasticity in the hippocampus.

Automated three-dimensional detection and counting of neuron somata

We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4%. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.

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.

The Mouse Limb Anatomy Atlas: an interactive 3D tool for studying embryonic limb patterning

Background

The developing mouse limb is widely used as a model system for studying tissue patterning. Despite this, few references are available that can be used for the correct identification of developing limb structures, such as muscles and tendons. Existing textual references consist of two-dimensional (2D) illustrations of the adult rat or mouse limb that can be difficult to apply when attempting to describe the complex three-dimensional (3D) relationship between tissues.

Results

To improve the resources available in the mouse model, we have generated a free, web-based, interactive reference of limb muscle, tendon, and skeletal structures at embryonic day (E) 14.5 . The Atlas was generated using mouse forelimb and hindlimb specimens stained using immunohistochemistry to detect muscle and tendon. Limbs were scanned using Optical Projection Tomography (OPT), reconstructed to make 3D models and annotated using computer-assisted segmentation tools in Amira 3D Visualisation software. The annotated dataset is visualised using Java, JAtlasView software. Users click on the names of structures and view their shape, position and relationship with other structures within the 3D model and also in 2D virtual sections.

Conclusion

The Mouse Limb Anatomy Atlas provides a novel and valuable tool for researchers studying limb development and can be applied to a range of research areas, including the identification of abnormal limb patterning in transgenic lines and studies of models of congenital limb abnormalities. By using the Atlas for "virtual" dissection, this resource offers an alternative to animal dissection. The techniques we have developed and employed are also applicable to many other model systems and anatomical structures.

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.

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Contact:pengh@janelia.hhmi.org

Supplementary information:Supplementary data are available at Bioinformatics online.

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.

Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals

Calcium ions (Ca2+) play a fundamental role in a variety of physiological functions in many cell types by acting as a secondary messenger. Variation of intracellular Ca2+ concentration ([Ca2+]i) is often observed when the cell is stimulated. However, it is a challenging task to automatically quantify intracellular [Ca2+]i in a population of cells. In this study, we present a workflow including specific algorithms for the automated intracellular calcium signal analysis using high-content, time-lapse cellular images. The experimental validations indicate the effectiveness of the proposed workflow and algorithms. We applied the workflow to analyze the intracellular calcium signals induced by different concentrations of H2O2 in the cell lines transfected by presenilin-1 (PS-1) that is known to be closely related to the familial Alzheimer's disease (FAD). The analysis results imply an important role of mutant PS-1, but not normal human PS-1 and mutant human amyloid precursor protein (APP), in enhancing intracellular calcium signaling induced by H2O2.

Three-dimensional tissue cytometer based on high-speed multiphoton microscopy

Image cytometry technology has been extended to 3D based on high-speed multiphoton microscopy. This technique allows in situ study of tissue specimens preserving important cell-cell and cell-extracellular matrix interactions. The imaging system was based on high-speed multiphoton microscopy (HSMPM) for 3D deep tissue imaging with minimal photodamage. Using appropriate fluorescent labels and a specimen translation stage, we could quantify cellular and biochemical states of tissues in a high throughput manner. This approach could assay tissue structures with subcellular resolution down to a few hundred micrometers deep. Its throughput could be quantified by the rate of volume imaging: 1.45 mm(3)/h with high resolution. For a tissue containing tightly packed, stratified cellular layers, this rate corresponded to sampling about 200 cells/s. We characterized the performance of 3D tissue cytometer by quantifying rare cell populations in 2D and 3D specimens in vitro. The measured population ratios, which were obtained by image analysis, agreed well with the expected ratios down to the ratio of 1/10(5). This technology was also applied to the detection of rare skin structures based on endogenous fluorophores. Sebaceous glands and a cell cluster at the base of a hair follicle were identified. Finally, the 3D tissue cytometer was applied to detect rare cells that had undergone homologous mitotic recombination in a novel transgenic mouse model, where recombination events could result in the expression of enhanced yellow fluorescent protein in the cells. 3D tissue cytometry based on HSMPM demonstrated its screening capability with high sensitivity and showed the possibility of studying cellular and biochemical states in tissues in situ. This technique will significantly expand the scope of cytometric studies to the biomedical problems where spatial and chemical relationships between cells and their tissue environments are important.

Three-dimensional digital image analysis of immunostained neurons in thick tissue sections

Detection and three dimensional reconstruction of cell structures from brightfield microscopy video clips using digital image processing algorithms is presented. While the confocal microscopy offers an efficient technique for three dimensional measurements, extensive and repeated measurements are still often better to be performed using permanent staining and brightfield microscopy. By processing of brightfield microscopy videos using automated and efficient digital image processing algorithms, the tedious task of manual analysis can be avoided. Our two-stage algorithm is applied for 1) cell soma detection and 2) identification of the 3D structure of entire neurons. To verify the results, we present 3D reconstructions of the detected cells.

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.

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick ( approximately 100 microm) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81 to 92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system.

Noise propagation and scaling in regulation of gonadotrope biosynthesis

Reproductive physiology depends on the control of biosynthesis in the pituitary gonadotrope by hypothalamic gonadotropin-releasing hormone (GnRH). The responses to GnRH include activation of extracellular signal-regulated kinase (ERK) and induction of Egr1. Using population and single cell signaling assays, we investigated the signal and noise transmission through this signaling and gene circuit, analyzing data obtained from 43,775 individual cells in 40 experiments. After exposure to GnRH, phosphorylated ERK (pERK) is elevated in 50% of the cells at 1.7 (SD = 0.3) min. Studies of the cell-to-cell response showed that for both pERK and for Egr1 protein production the mean response (mu) and standard deviation (sigma) within individual cells were linearly related (sigma = kmu) and had similar values of k. To understand the basis for the scaling observed for noise propagation through this system, we determined the relationship between pERK and egr1 mRNA levels induced at varying concentration of GnRH. While both pERK and egr1 mRNA show a saturating sigmoidal relationship to the concentration of GnRH exposure, egr1 mRNA is linearly related to the levels of pERK. These results explain the basis for variation in cellular responses in an important mammalian signaling pathway leading to gene induction.

Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.

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.

Experimental platform for the study of region specific excitation and inhibition in neural tissue

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this is due to the fact that experimental validation of the evoked response to stimuli is an arduous and time-consuming task. The development of a high-throughput data acquisition and analysis tool would greatly facilitate the design of spatially selective stimulation protocols. We present an automated imaging system that can optically track and identify the action potentials of individual neurons evoked by coordinated stimulus waveforms applied at multiple electrodes. This system can simultaneously provide arbitrary current waveforms to four electrodes, and it is capable of automatically monitoring the cellular responses of every neuron in a cultured network within a 1.6 x 1.6 mm area. The purpose of this platform is to develop stimulus protocols that exploit the benefits of multi-polar field shaping and temporal ion-channel manipulation to localize cellular excitation beyond the vicinity of the electrode. Preliminary single electrode experiments demonstrate that spatially selective stimulus suppression may be achieved with cathodic, depolarizing prepulses that induce a sub-threshold refractory state in neighboring neurons. Coordinated, multi-site stimuli could potentially take advantage of this refractory state to direct the stimulus focus away from the surrounding area of the electrode and into the inter-electrode spaces.

Imaging in systems biology

Most systems biology approaches involve determining the structure of biological circuits using genomewide "-omic" analyses. Yet imaging offers the unique advantage of watching biological circuits function over time at single-cell resolution in the intact animal. Here, we discuss the power of integrating imaging tools with more conventional -omic approaches to analyze the biological circuits of microorganisms, plants, and animals.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles

Background

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.

Results

The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.

Conclusion

The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

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.

Spinal and Supraspinal Effects of Long-Term Stimulation of Sensorimotor Cortex in Rats

Sensorimotor cortex (SMC) modifies spinal cord reflex function throughout life and is essential for operant conditioning of the H-reflex. To further explore this long-term SMC influence over spinal cord function and its possible clinical uses, we assessed the effect of long-term SMC stimulation on the soleus H-reflex. In freely moving rats, the soleus H-reflex was measured 24 h/day for 12 wk. The soleus background EMG and M response associated with H-reflex elicitation were kept stable throughout. SMC stimulation was delivered in a 20-day-on/20-day-off/20-day-on protocol in which a train of biphasic 1-ms pulses at 25 Hz for 1 s was delivered every 10 s for the on-days. The SMC stimulus was automatically adjusted to maintain a constant descending volley. H-reflex size gradually increased during the 20 on-days, stayed high during the 20 off-days, and rose further during the next 20 on-days. In addition, the SMC stimulus needed to maintain a stable descending volley rose steadily over days. It fell during the 20 off-days and rose again when stimulation resumed. These results suggest that SMC stimulation, like H-reflex operant conditioning, induces activity-dependent plasticity in both the brain and the spinal cord and that the plasticity responsible for the H-reflex increase persists longer after the end of SMC stimulation than that underlying the change in the SMC response to stimulation.