Active mask segmentation of fluorescence microscope images

Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells

Classification of live or fixed cells based on their unlabeled microscopic images would be a powerful tool for cell biology and pathology. For such software, the first step is the generation of a ground truth database that can be used for training and testing AI classification algorithms. The Application of cells expressing fluorescent reporter proteins allows the building of ground truth datasets in a straightforward way. In this study, we present an automated imaging pipeline utilizing the Cellpose algorithm for the precise cell segmentation and measurement of fluorescent cellular intensities across multiple channels. We analyzed the cell cycle of HeLa-FUCCI cells expressing fluorescent red and green reporter proteins at various levels depending on the cell cycle state. To build the dataset, 37,000 fixed cells were automatically scanned using a standard motorized microscope, capturing phase contrast and fluorescent red/green images. The fluorescent pixel intensity of each cell was integrated to calculate the total fluorescence of cells based on cell segmentation in the phase contrast channel. It resulted in a precise intensity value for each cell in both channels. Furthermore, we conducted a comparative analysis of Cellpose 1.0 and Cellpose 2.0 in cell segmentation performance. Cellpose 2.0 demonstrated notable improvements, achieving a significantly reduced false positive rate of 2.7 % and 1.4 % false negative. The cellular fluorescence was visualized in a 2D plot (map) based on the red and green intensities of the FUCCI construct revealing the continuous distribution of cells in the cell cycle. This 2D map enables the selection and potential isolation of single cells in a specific phase. In the corresponding heatmap, two clusters appeared representing cells in the red and green states. Our pipeline allows the high-throughput and accurate measurement of cellular fluorescence providing extensive statistical information on thousands of cells with potential applications in developmental and cancer biology. Furthermore, our method can be used to build ground truth datasets automatically for training and testing AI cell classification. Our automated pipeline can be used to analyze thousands of cells within 2 h after putting the sample onto the microscope.

Joint Segmentation and Pairing of Nuclei and Golgi in 3D Microscopy Images

Blood vessels provide oxygen and nutrients to all tissues in the human body, and their incorrect organisation or dysfunction contributes to several diseases. Correct organisation of blood vessels is achieved through vascular patterning, a process that relies on endothelial cell polarization and migration against the blood flow direction. Unravelling the mechanisms governing endothelial cell polarity is essential to study the process of vascular patterning. Cell polarity is defined by a vector that goes from the nucleus centroid to the corresponding Golgi complex centroid, here defined as axial polarity. Currently, axial polarity is calculated manually, which is time-consuming and subjective. In this work, we used a deep learning approach to segment nuclei and Golgi in 3D fluorescence microscopy images of mouse retinas, and to assign nucleus-Golgi pairs. This approach predicts nuclei and Golgi segmentation masks but also a third mask corresponding to joint nuclei and Golgi segmentations. The joint segmentation mask is used to perform nucleus-Golgi pairing. We demonstrate that our deep learning approach using three masks successfully identifies nucleus-Golgi pairs, outperforming a pairing method based on a cost matrix. Our results pave the way for automated computation of axial polarity in 3D tissues and in vivo.

CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells

The fundamental basis in the development of novel radiotherapy methods is in-vitro cellular studies. To assess different endpoints of cellular reactions to irradiation like proliferation, cell cycle arrest, and cell death, several assays are used in radiobiological research as standard methods. For example, colony forming assay investigates cell survival and Caspase3/7-Sytox assay cell death. The major limitation of these assays is the analysis at a fixed timepoint after irradiation. Thus, not much is known about the reactions before or after the assay is performed. Additionally, these assays need special treatments, which influence cell behavior and health. In this study, a completely new method is proposed to tackle these challenges: A deep-learning algorithm called CeCILE (Cell Classification and In-vitro Lifecycle Evaluation), which is used to detect and analyze cells on videos obtained from phase-contrast microscopy. With this method, we can observe and analyze the behavior and the health conditions of single cells over several days after treatment, up to a sample size of 100 cells per image frame. To train CeCILE, we built a dataset by labeling cells on microscopic images and assign class labels to each cell, which define the cell states in the cell cycle. After successful training of CeCILE, we irradiated CHO-K1 cells with 4 Gy protons, imaged them for 2 days by a microscope equipped with a live-cell-imaging set-up, and analyzed the videos by CeCILE and by hand. From analysis, we gained information about cell numbers, cell divisions, and cell deaths over time. We could show that similar results were achieved in the first proof of principle compared with colony forming and Caspase3/7-Sytox assays in this experiment. Therefore, CeCILE has the potential to assess the same endpoints as state-of-the-art assays but gives extra information about the evolution of cell numbers, cell state, and cell cycle. Additionally, CeCILE will be extended to track individual cells and their descendants throughout the whole video to follow the behavior of each cell and the progeny after irradiation. This tracking method is capable to put radiobiologic research to the next level to obtain a better understanding of the cellular reactions to radiation.

Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis

Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Amongst these, schistosomiasis (bilharzia or 'snail fever'), caused by blood flukes of the genus Schistosoma, ranks second only to malaria in terms of human impact: two hundred million people are infected and close to 800 million are at risk of infection. Drug screening against helminths poses unique challenges: the parasite cannot be cloned and is difficult to target using gene knockouts or RNAi. Consequently, both lead identification and validation involve phenotypic screening, where parasites are exposed to compounds whose effects are determined through the analysis of the ensuing phenotypic responses. The efficacy of leads thus identified derives from one or more or even unknown molecular mechanisms of action. The two most immediate and significant challenges that confront the state-of-the-art in this area are: the development of automated and quantitative phenotypic screening techniques and the mapping and quantitative characterization of the totality of phenotypic responses of the parasite. In this paper, we investigate and propose solutions for the latter problem in terms of the following: (1) mathematical formulation and algorithms that allow rigorous representation of the phenotypic response space of the parasite, (2) application of graph-theoretic and network analysis techniques for quantitative modeling and characterization of the phenotypic space, and (3) application of the aforementioned methodology to analyze the phenotypic space of S. mansoni - one of the etiological agents of schistosomiasis, induced by compounds that target its polo-like kinase 1 (PLK 1) gene - a recently validated drug target. In our approach, first, bio-image analysis algorithms are used to quantify the phenotypic responses of different drugs. Next, these responses are linearly mapped into a low- dimensional space using Principle Component Analysis (PCA). The phenotype space is modeled using neighborhood graphs which are used to represent the similarity amongst the phenotypes. These graphs are characterized and explored using network analysis algorithms. We present a number of results related to both the nature of the phenotypic space of the S. mansoni parasite as well as algorithmic issues encountered in constructing and analyzing the phenotypic-response space. In particular, the phenotype distribution of the parasite was found to have a distinct shape and topology. We have also quantitatively characterized the phenotypic space by varying critical model parameters. Finally, these maps of the phenotype space allows visualization and reasoning about complex relationships between putative drugs and their system-wide effects and can serve as a highly efficient paradigm for assimilating and unifying information from phenotypic screens both during lead identification and lead optimization.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

BACKGROUND

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility.

NEW METHOD

Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set.

RESULTS

Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings.

COMPARISON WITH EXISTING METHODS

We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples.

CONCLUSION

The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Coupled Segmentation of Nuclear and Membrane-bound Macromolecules through Voting and Multiphase Level Set

Membrane-bound macromolecules play an important role in tissue architecture and cell-cell communication, and is regulated by almost one-third of the genome. At the optical scale, one group of membrane proteins expresses themselves as linear structures along the cell surface boundaries, while others are sequestered; and this paper targets the former group. Segmentation of these membrane proteins on a cell-by-cell basis enables the quantitative assessment of localization for comparative analysis. However, such membrane proteins typically lack continuity, and their intensity distributions are often very heterogeneous; moreover, nuclei can form large clump, which further impedes the quantification of membrane signals on a cell-by-cell basis. To tackle these problems, we introduce a three-step process to (i) regularize the membrane signal through iterative tangential voting, (ii) constrain the location of surface proteins by nuclear features, where clumps of nuclei are segmented through a delaunay triangulation approach, and (iii) assign membrane-bound macromolecules to individual cells through an application of multi-phase geodesic level-set. We have validated our method using both synthetic data and a dataset of 200 images, and are able to demonstrate the efficacy of our approach with superior performance.

Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration.

Images as occlusions of textures: a framework for segmentation

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

Characterization of enteric neurons in wild-type and mutant zebrafish using semi-automated cell counting and co-expression analysis

To characterize fluorescent enteric neurons labeled for expression of cytoplasmic markers in zebrafish mutants, we developed a new MATLAB-based program that can be trained by user input. We used the program to count enteric neurons and to analyze co-expression of the neuronal marker, Elavl, and the neuronal subtype marker, serotonin, in 3D confocal image stacks of dissected whole-mount zebrafish intestines. We quantified the entire population of enteric neurons and the serotonergic subpopulation in specific regions of the intestines of gutwrencher mutant and wild-type sibling larvae. We show a marked decrease in enteric neurons in gutwrencher mutants that is more severe at the caudal end of the intestine. We also show that gutwrencher mutants have the same number of serotonin-positive enteroendocrine cells in the intestine as wild types.

Segmenting the etiological agent of schistosomiasis for high-content screening

Schistosomiasis is a parasitic disease with a global health impact second only to malaria. The World Health Organization has classified schistosomiasis as an illness for which new therapies are urgently needed. However, the causative parasite is refractory to current high-throughput drug screening due to the diversity and complexity of shape, appearance and movement-based phenotypes exhibited in response to putative drugs. Currently, there is no automated image-based approach capable of relieving this deficiency. We propose and validate an image segmentation algorithm designed to overcome the distinct challenges posed by schistosomes and macroparasites in general, including irregular shapes and sizes, dense groups of touching parasites and the unpredictable effects of drug exposure. Our approach combines a region-based distributing function with a novel edge detector derived from phase congruency and grayscale thinning by threshold superposition. The method is sufficiently rapid, robust and accurate to be used for quantitative analysis of diverse parasite phenotypes in high-throughput and high-content screening.

Local histograms and image occlusion models

The local histogram transform of an image is a data cube that consists of the histograms of the pixel values that lie within a fixed neighborhood of any given pixel location. Such transforms are useful in image processing applications such as classification and segmentation, especially when dealing with textures that can be distinguished by the distributions of their pixel intensities and colors. We, in particular, use them to identify and delineate biological tissues found in histology images obtained via digital microscopy. In this paper, we introduce a mathematical formalism that rigorously justifies the use of local histograms for such purposes. We begin by discussing how local histograms can be computed as systems of convolutions. We then introduce probabilistic image models that can emulate textures one routinely encounters in histology images. These models are rooted in the concept of image occlusion. A simple model may, for example, generate textures by randomly speckling opaque blobs of one color on top of blobs of another. Under certain conditions, we show that, on average, the local histograms of such model-generated-textures are convex combinations of more basic distributions. We further provide several methods for creating models that meet these conditions; the textures generated by some of these models resemble those found in histology images. Taken together, these results suggest that histology textures can be analyzed by decomposing their local histograms into more basic components. We conclude with a proof-of-concept segmentation-and-classification algorithm based on these ideas, supported by numerical experimentation.

A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model that captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei.

Enhancing automated micrograph-based evaluation of LPS-stimulated macrophage spreading

To evaluate macrophage spreading in immunofluorescence images of macrophages for surface protein CD11b and nuclear counterstaining with DAPI, it is necessary to measure the size of the macrophages at different time points after stimulation. Manual evaluation of fluorescent micrographs is usually a time-consuming and error-prone task, with poor reproducibility. Automatic image analysis methods can be used to improve the results. The quality of the analysis with these methods mainly depends on the quality of the image segmentation. A segmentation and quantification scheme based on shading correction, k-means clustering, and fast marching level sets has been developed for the purpose. An initial application of this approach showed that separating touching and overlapping cells in particular suffers severely in the inevitably blurred conditions, leading to partly erroneous measurements of macrophage spreading. An alternative method of segmentation in fluorescent micrographs was therefore investigated and evaluated in this study. The proposed approach uses a methodology that separates foreground objects from background objects on the basis of Boykov's graph cuts. In this process, a rough estimation of background pixels is used for background seeds. To identify foreground seeds, a difference of Gaussian band pass filter based workflow is developed. Information on foreground and background seeds is then used for a gradient magnitude based graph cut resulting in a robust figure-ground separation method. In addition, a fast marching level set approach is used in the post-processing step, which makes it possible to split touching cells by incorporating information about the cell nuclei. An evaluation based on a total of 553 manually labeled macrophages depicted in 21 micrographs showed that the proposed method significantly improves segmentation and splitting performance for fluorescent micrographs of LPS-stimulated macrophages and reduces the rate of error in automated analysis of macrophage spreading in comparison with alternative methods.

Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation

In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear convolution composed with a nonlinear thresholding; what makes this process special in our case is the presence of additive terms whose role is to "skew" the voting when prior information is available. In real-world implementation, the AM algorithm always converges to a fixed point. We study the behavior of AM rigorously and present a proof of this convergence. The key idea is to formulate AM as a generalized (parallel) majority cellular automaton, adapting proof techniques from discrete dynamical systems.

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach.

Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis

Background

Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery.

Method

We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis.

Results

We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs.

Conclusions

The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells.

Comparison of segmentation algorithms for fluorescence microscopy images of cells

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.

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.

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.

CONVERGENCE BEHAVIOR OF THE ACTIVE MASK SEGMENTATION ALGORITHM

We study the convergence behavior of the Active Mask (AM) framework, originally designed for segmenting punctate image patterns. AM combines the flexibility of traditional active contours, the statistical modeling power of region-growing methods, and the computational efficiency of multiscale and multiresolution methods. Additionally, it achieves experimental convergence to zero-change (fixed-point) configurations, a desirable property for segmentation algorithms. At its a core lies a voting-based distributing function which behaves as a majority cellular automaton. This paper proposes an empirical measure correlated to the convergence behavior of AM, and provides sufficient theoretical conditions on the smoothing filter operator to enforce convergence.

Active mask segmentation of fluorescence microscope images

We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

Multiresolution Multiscale Active Mask Segmentation of Fluorescence Microscope Images

We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images.