Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours

Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

ASIST: Annotation-free synthetic instance segmentation and tracking by adversarial simulations

BACKGROUND

The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of two stages: (1) performing instance object segmentation of each frame, and (2) associating objects frame-by-frame. Recently, pixel-embedding-based deep learning approaches these two steps simultaneously as a single stage holistic solution. Pixel-embedding-based learning forces similar feature representation of pixels from the same object, while maximizing the difference of feature representations from different objects. However, such deep learning methods require consistent annotations not only spatially (for segmentation), but also temporally (for tracking). In computer vision, annotated training data with consistent segmentation and tracking is resource intensive, the severity of which is multiplied in microscopy imaging due to (1) dense objects (e.g., overlapping or touching), and (2) high dynamics (e.g., irregular motion and mitosis). Adversarial simulations have provided successful solutions to alleviate the lack of such annotations in dynamics scenes in computer vision, such as using simulated environments (e.g., computer games) to train real-world self-driving systems.

METHODS

In this paper, we propose an annotation-free synthetic instance segmentation and tracking (ASIST) method with adversarial simulation and single-stage pixel-embedding based learning.

CONTRIBUTION

The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning (2) the method is assessed with both the cellular (i.e., HeLa cells); and subcellular (i.e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.

RESULTS

The ASIST method achieved an important step forward, when compared with fully supervised approaches: ASIST shows 7%-11% higher segmentation, detection and tracking performance on microvilli relative to fully supervised methods, and comparable performance on Hela cell videos.

Automated classification of mitotic catastrophe by use of the centromere fragmentation morphology

Mitotic catastrophe is a common mode of tumor cell death. Cancer cells with a defective cell-cycle checkpoint often enter mitosis with damaged or under replicated chromosomes following genotoxic treatment. Premature condensation of the under-replicated (or damaged) chromosomes results in double-stranded DNA breaks at the centromere (centromere fragmentation). Centromere fragmentation is a morphological marker of mitotic catastrophe and is distinguished by the clustering of centromeres away from the chromosomes. We present an automated 2-step system for segmentation of cells exhibiting centromere fragmentation. The first step segments individual cells from clumps. We added two new terms, weighted local repelling term (WLRt) and weighted gradient term (WGt), in the energy functional of the traditional Chan-Vese based level set method. WLRt was used to generate a repelling force when contours of adjacent cells merged and then penalized the overlap. WGt enhances gradients between overlapping cells. The second step consists of a new algorithm, SBaN (shape-based analysis of each nucleus), which extracts features like circularity, major-axis length, minor-axis length, area, and eccentricity from each chromosome to identify cells with centromere fragmentation. The performance of SBaN algorithm for centromere fragmentation detection was statistically evaluated and the results were robust.

Image segmentation using active contours with modified convolutional virtual electric field external force with an edge-stopping function

The gradient vector flow (GVF) is an effective external force to deform the active contours. However, it suffers from high computational cost. For efficiency, the virtual electric field (VEF) model has been proposed, which can be implemented in real time thanks to fast Fourier transform (FFT). The VEF model has large capture range and low computation cost, but it has the limitations of sensitivity to noise and leakage on weak edge. The recently proposed CONVEF (Convolutional Virtual Electric Field) model takes the VEF model as a convolutional operation and employed another convolution kernel to overcome the drawbacks of the VEF model. In this paper, we employ an edge stopping function similar to that in the anisotropic diffusion to further improve the CONVEF model, and the proposed model is referred to as MCONVEF (Modified CONVEF) model. In addition, a piecewise constant approximation algorithm is borrowed to accelerate the calculation of the MCONVEF model. The proposed MCONVEF model is compared with the GVF and VEF models, and the experimental results are presented to demonstrate its superiority in terms of noise robustness, weak edge preserving and deep concavity convergence.

A Dynamic-Shape-Prior Guided Snake Model with Application in Visually Tracking Dense Cell Populations

This work proposes a dynamic-shape-prior guided snake model (DSP G-snake) that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake's snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from self-crossing, or automatically untie an already selfintersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as state-of-the-art cell tracking frameworks.

Tracking of non-dividing cells by using generalized Voronoi diagram

Cell tracking is an important technique to study cell migration. However, to obtain good tracking results is still a challenging task. False positives and false negatives are two main factors that affect cell tracking accuracy. It is desirable to have a good method to eliminate false positives and false negatives effectively. We propose to use generalized Voronoi diagrams (GVDs) to perform tracking for non-dividing cells in a key point evolving based algorithm. This method can realize tracking association for multiple objects based cell racking and eliminate false positives and false negatives effectively based on a defined cost function. We investigated under what conditions our algorithm would work, and what were the results when the assumptions for the algorithm were violated. This provides the relationship between the cell migration speed and movie temporal resolution. We further validated that our algorithm works well under reasonable assumptions. We tested our algorithm with both 2D+time and 3D+time data sets. In conclusion, the use of Generalized Voronoi Diagram in cell tracking is an effective technique to increase tracking accuracy. The temporal resolution when acquiring movies should be ensured in order to achieve good tracking results.

Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration

Understanding the mechanisms of collective cell migration is crucial for cancer metastasis, wound healing and many developmental processes. Imaging a migrating cluster in vivo is feasible, but the quantification of individual cell behaviours remains challenging. We have developed an image analysis toolkit, CCMToolKit, to quantify the Drosophila border cell system. In addition to chaotic motion, previous studies reported that the migrating cells are able to migrate in a highly coordinated pattern. We quantify the rotating and running migration modes in 3D while also observing a range of intermediate behaviours. Running mode is driven by cluster external protrusions. Rotating mode is associated with cluster internal cell extensions that could not be easily characterized. Although the cluster moves slower while rotating, individual cells retain their mobility and are in fact slightly more active than in running mode. We also show that individual cells may exchange positions during migration.

Dynamic Programming Using Polar Variance for Image Segmentation

When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment the objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared with other segmentation techniques.

Hermite Snakes With Control of Tangents

We introduce a new model of parametric contours defined in a continuous fashion. Our curve model relies on Hermite spline interpolation and can easily generate curves with sharp discontinuities; it also grants direct access to the tangent at each location. With these two features, the Hermite snake distinguishes itself from classical spline-snake models and allows one to address certain bioimaging problems in a more efficient way. More precisely, the Hermite snake construction allows introducing sharp corners in the snake curve and designing directional energy functionals relying on local orientation information in the input image. Using the formalism of spline theory, the model is shown to meet practical requirements such as invariance to affine transformations and good approximation properties. Finally, the dependence on initial conditions and the robustness to the noise is studied on synthetic data in order to validate our Hermite snake model, and its usefulness is illustrated on real biological images acquired using brightfield, phase-contrast, differential-interference-contrast, and scanning-electron microscopy.

Guide filter-based gradient vector flow module for infrared image segmentation

Infrared image segmentation is a challenging topic since infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow (GVF), have better segmentation performance for clear images. However, the GVF model has the drawbacks of sensitivity to noise and adaptability of the parameters, decreasing the effect of infrared image segmentation significantly. To address these problems, this paper proposes a guide filter-based gradient vector flow module for infrared image segmentation (GFGVF). First, a guide filter is exploited to construct a novel edge map, providing characteristics of the image edge while excluding the effects of noise. This alleviates the possibility of edge leakage caused by using the traditional edge map. Then, a novel weighting function is constructed to effectively handle the extended capture range and preserving the edge even with noise existing. The experimental results demonstrate that the GFGVF model possesses good properties such as large capture range, concavity convergence, noise robustness, and alleviative boundary leakage.

Light sheet fluorescence microscopy (LSFM): past, present and future

Light sheet fluorescence microscopy (LSFM) has emerged as an important imaging modality to follow biology in live 3D samples over time with reduced phototoxicity and photobleaching. In particular, LSFM has been instrumental in revealing the detail of early embryonic development of Zebrafish, Drosophila, and C. elegans. Open access projects, DIY-SPIM, OpenSPIM, and OpenSPIN, now allow LSFM to be set-up easily and at low cost. The aim of this paper is to facilitate the set-up and use of LSFM by reviewing and comparing open access projects, image processing tools and future challenges.

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

Dynamic behavior of stem cells during in vitro development is diverse. Previous cell tracking studies have focused more on cell proliferation than on cell aggregation. However, the enhancement of cell proliferation in association with cell aggregation has been reported. In a previous study, we also demonstrated that the aggregation of adult human mesenchymal stem cells to form three-dimensional (3D) cellular spheroids helped maintain the expression of stemness marker genes in the cells. However, the dynamic behavioral changes triggered by spheroid formation remain to be investigated. A scheme of image processing techniques is proposed to meet this need. A hybrid-thresholding technique was first developed for efficient segmentation of cell clusters, after which a cell tracking method based on pair-matching with topological constraints was designed. Two morphological indices were derived to track the timing of 3D spheroid formation during the cellular aggregation process. Five cell motility indices measured from single cells and 3D spheroids were then compared. After confirmation of more than 90% correspondence between the results obtained by manual tracking and the proposed methods, an analysis of cellular behavior reveals a significant increase in motility in association with spheroid formation, consistent with a previous report that used a gene expression approach. This study proposed a systematic image analysis method to quantify the dynamic behavior of stem cells for stemness evaluation during cell culturing in vitro. Results demonstrated the validity of the developed platform in investigation of the dynamic behavior of cell aggregation in stem cell cultures in vitro.

Convolutional virtual electric field for image segmentation using active contours

Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images.

A novel approach to segment and classify regional lymph nodes on computed tomography images

Morphology of lymph nodal metastasis is critical for diagnosis and prognosis of cancer patients. However, accurate prediction of lymph node type based on morphological information is rarely available due to lack of pathological validation. To obtain correct morphological information, lymph nodes must be segmented from computed tomography (CT) image accurately. In this paper we described a novel approach to segment and predict the status of lymph nodes from CT images and confirmed the diagnostic performance by clinical pathological results. We firstly removed noise and preserved edge details using a revised nonlinear diffusion equation, and secondly we used a repulsive-force-based snake method to segment the lymph nodes. Morphological measurements for the characterization of the node status were obtained from the segmented node image. These measurements were further selected to derive a highly representative set of node status, called feature vector. Finally, classical classification scheme based on support vector machine model was employed to simulate the prediction of nodal status. Experiments on real clinical rectal cancer data showed that the prediction performance with the proposed framework is highly consistent with pathological results. Therefore, this novel algorithm is promising for status prediction of lymph nodes.

Motion imitation with a handheld camera

In this paper, we present a novel method to extract motion of a dynamic object from a video that is captured by a handheld camera, and apply it to a 3D character. Unlike the motion capture techniques, neither special sensors/trackers nor a controllable environment is required. Our system significantly automates motion imitation which is traditionally conducted by professional animators via manual keyframing. Given the input video sequence, we track the dynamic reference object to obtain trajectories of both 2D and 3D tracking points. With them as constraints, we then transfer the motion to the target 3D character by solving an optimization problem to maintain the motion gradients. We also provide a user-friendly editing environment for users to fine tune the motion details. As casual videos can be used, our system, therefore, greatly increases the supply source of motion data. Examples of imitating various types of animal motion are shown.

Tracking colliding cells in vivo microscopy

Leukocyte motion represents an important component in the innate immune response to infection. Intravital microscopy is a powerful tool as it enables in vivo imaging of leukocyte motion. Under inflammatory conditions, leukocytes may exhibit various motion behaviors, such as flowing, rolling, and adhering. With many leukocytes moving at a wide range of speeds, collisions occur. These collisions result in abrupt changes in the motion and appearance of leukocytes. Manual analysis is tedious, error prone,time consuming, and could introduce technician-related bias. Automatic tracking is also challenging due to the noise inherent in in vivo images and abrupt changes in motion and appearance due to collision. This paper presents a method to automatically track multiple cells undergoing collisions by modeling the appearance and motion for each collision state and testing collision hypotheses of possible transitions between states. The tracking results are demonstrated using in vivo intravital microscopy image sequences.We demonstrate that 1)71% of colliding cells are correctly tracked; (2) the improvement of the proposed method is enhanced when the duration of collision increases; and (3) given good detection results, the proposed method can correctly track 88% of colliding cells. The method minimizes the tracking failures under collisions and, therefore, allows more robust analysis in the study of leukocyte behaviors responding to inflammatory conditions.

Geometrically induced force interaction for three-dimensional deformable models

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

Automated kymograph analysis for profiling axonal transport of secretory granules

This paper describes an automated method to profile the velocity patterns of small organelles (BDNF granules) being transported along a selected section of axon of a cultured neuron imaged by time-lapse fluorescence microscopy. Instead of directly detecting the granules as in conventional tracking, the proposed method starts by generating a two-dimensional spatio-temporal map (kymograph) of the granule traffic along an axon segment. Temporal sharpening during the kymograph creation helps to highlight granule movements while suppressing clutter due to stationary granules. A voting algorithm defined over orientation distribution functions is used to refine the locations and velocities of the granules. The refined kymograph is analyzed using an algorithm inspired from the minimum set cover framework to generate multiple motion trajectories of granule transport paths. The proposed method is computationally efficient, robust to significant levels of noise and clutter, and can be used to capture and quantify trends in transport patterns quickly and accurately. When evaluated on a collection of image sequences, the proposed method was found to detect granule movement events with 94% recall rate and 82% precision compared to a time-consuming manual analysis. Further, we present a study to evaluate the efficacy of velocity profiling by analyzing the impact of oxidative stress on granule transport in which the fully automated analysis correctly reproduced the biological conclusion generated by manual analysis.

A probabilistic cell model in background corrected image sequences for single cell analysis

Background

Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame.

Methods

Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study). To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background.

Results

The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.

Conclusion

The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable of localizing single cells in microwells and can be adapted for the other cell types that may not have circular shape. This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

Live-cell tracking using SIFT features in DIC microscopic videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.

Tracking in cell and developmental biology

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Automatic active model initialization via Poisson inverse gradient

Active models have been widely used in image processing applications. A crucial stage that affects the ultimate active model performance is initialization. This paper proposes a novel automatic initialization approach for parametric active models in both 2-D and 3-D. The PIG initialization method exploits a novel technique that essentially estimates the external energy field from the external force field and determines the most likely initial segmentation. Examples and comparisons with two state-of-the- art automatic initialization methods are presented to illustrate the advantages of this innovation, including the ability to choose the number of active models deployed, rapid convergence, accommodation of broken edges, superior noise robustness, and segmentation accuracy.

MAC: magnetostatic active contour model

We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of GVF snake, the combined geodesic and GVF snake, and the charged particle model.

Liver microcirculation analysis by red blood cell motion modeling in intravital microscopy images

Intravital microscopy has been used to visualize the microcirculation by imaging fluorescent labeled red blood cells (RBCs). Traditionally, microcirculation has been modeled by computing the mean velocity of a few, randomly selected, manually tracked RBCs. However, this protocol is tedious, time consuming, and subjective with technician related bias. We present a new method for analyzing the microcirculation by modeling the RBC motion through automatic tracking. The tracking of RBCs is challenging as in each image, as many as 200 cells move through a complex network of vessels at a wide range of speeds while deforming in shape. To reliably detect RBCs traveling at a wide range of speeds, a window of temporal template matching is applied. Then, cells appearing in successive frames are corresponded based on the motion behavior constraints in terms of the direction, magnitude, and path. The performance evaluation against a ground truth indicates the detection accuracy up to 84% TP at 6% FP and a correspondence accuracy of 89%. We include an in-depth discussion on comparison of the microcirculation based on motion modeling from the proposed automated method against a mean velocity from manual analysis protocol in terms of precision, objectivity, and sensitivity.

Extended-Hungarian-JPDA: exact single-frame stem cell tracking

The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.

Active contour external force using vector field convolution for image segmentation

Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.

Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks

The branching patterns of axons and dendrites are fundamental structural properties that affect the synaptic connectivity of axons. Although today three-dimensional images of fluorescently labeled processes can be obtained to study axonal branching, there are no robust methods of tracing individual axons. This paper describes a repulsive force based snake model to segment and track axonal profiles in 3D images. This new method segments all the axonal profiles in a 2D image and then uses the results obtained from that image as prior information to help segment the adjacent 2D image. In this way, the segmentation successfully connects axonal profiles over hundreds of images in a 3D image stack. Individual axons can then be extracted based on the segmentation results. The utility and performance of the method are demonstrated using 3D axonal images obtained from transgenic mice that express fluorescent protein.

Functional proteometrics for cell migration

BACKGROUND

Advances in living cellular fluorescence biosensors and computerized microscopy enable a vision of fully automated high-resolution measurements of the detailed intracellular molecular dynamics directly linked to cellular behaviors. Given the heterogeneity of cell populations, a statistically relevant study of molecular-cellular dynamics is a key motivation for improved automation.

METHODS

We explored automating computerized, microscope-based data extraction and analyses that monitor cell locomotion, rates of mitoses, and spatiotemporal activities of intracellular proteins via ratiometric fluorescent biosensors in mouse fibroblasts. Novel image processing methods included K-means clustering segmentation preprocessing followed by modified discrete, normalized cross-correlational alignment of two-color images; ratiometric processing for fluorescence resonance energy transfer (FRET) measurements; and intracellular spatial distribution measurements of RhoA GTPase activity.

RESULTS

The interdivision time was 19.4 h (mean) +/- 6.0 h (SD) (n = 7) for the GFP-histone cells in the two-by-two field that was scanned for 72 h. After registration and ratioing of the cells with the RhoA biosensor, increases in both cell protrusion and retraction were coincident with to increases in RhoA activity.

CONCLUSIONS

These advances lay the foundation for extracting and correlating measurements characterizing the functional relationships of spatial localization and protein activation with features of cell migration such as velocity, polarization, protrusion, retraction, and mitosis.

Analysis of leukocyte rolling in vivo and in vitro

Leukocyte rolling is an important step for the successful recruitment of leukocytes from blood to tissues mediated by a specialized group of glycoproteins termed selectins. Because of the dynamic process of leukocyte rolling, binding of selectins to their respective counter-receptors (selectin ligands) needs to fulfill three major requirements: (1) rapid bond formation, (2) high tensile strength, and (3) fast dissociation rates. These criteria are perfectly met by selectins, which interact with specific carbohydrate determinants on selectin ligands. This chapter describes the theoretical background, technical requirements, and analytical tools needed to quantitatively assess leukocyte rolling in vivo and in vitro. For the in vivo setting, intravital microscopy allows the observation and recording of leukocyte rolling under different physiological and pathological conditions in almost every organ. Real-time and off-line analysis tools help to assess geometric, hemodynamic, and rolling parameters. Under in vitro conditions, flow chamber assays such as parallel plate flow chamber systems have been the mainstay to study interactions between leukocytes and adhesion molecules under flow. In this setting, adhesion molecules are immobilized on plastic, in a lipid monolayer, or presented on cultured endothelial cells on the chamber surface. Microflow chambers are available for studying leukocyte adhesion in the context of whole blood and without blood cell isolation. The microscopic observation of leukocyte rolling in different in vivo and in vitro settings has significantly contributed to our understanding of the molecular mechanisms responsible for the stepwise extravasation of leukocytes into inflamed tissues.

Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.

Intravital leukocyte detection using the gradient inverse coefficient of variation

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.