Tracking leukocytes in vivo 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.

Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking

Ca2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus' boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca2+ signal. The analysis package represents a significant improvement to available Ca2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca2+ signal extraction workflows.

Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration

Motile cells have developed a variety of migration modes relying on diverse traction-force-generation mechanisms. Before the behavior of intracellular components could be easily imaged, cell movements were mostly classified by different types of cellular shape dynamics. Indeed, even though some types of cells move without any significant change in shape, most cell propulsion mechanisms rely on global or local deformations of the cell surface. In this review, focusing mostly on metazoan cells, we discuss how different types of local and global shape changes underlie distinct migration modes. We then discuss mechanical differences between force-generation mechanisms and finish by speculating on how they may have evolved.

Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images

Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.

Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.

SITUP: Scale Invariant Tracking using Average Peak-to-Correlation Energy

Robust and accurate scale estimation of a target object is a challenging task in visual object tracking. Most existing tracking methods cannot accommodate large scale variation in complex image sequences and thus result in inferior performance. In this paper, we propose to incorporate a novel criterion called the average peak-to-correlation energy into the multi-resolution translation filter framework to obtain robust and accurate scale estimation. The resulting system is named SITUP: Scale Invariant Tracking using Average Peak-to-Correlation Energy. SITUP effectively tackles the problem of fixed template size in standard discriminative correlation filter based trackers. Extensive empirical evaluation on the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework meets the demands of scale variation challenges effectively while providing superior performance over other scale adaptive variants of standard discriminative correlation filter based trackers. Also, SITUP obtains favorable performance compared to state-of-the-art trackers for various scenarios while operating in real-time on a single CPU.

Quantification of sterol-specific response in human macrophages using automated imaged-based analysis

BACKGROUND

The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol.

METHODS

Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions.

RESULTS

We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol.

CONCLUSIONS

The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

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.

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.

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.

Unbiased estimation of cell number using the automatic optical fractionator

A novel stereology approach, the automatic optical fractionator, is presented for obtaining unbiased and efficient estimates of the number of cells in tissue sections. Used in combination with existing segmentation algorithms and ordinary immunostaining methods, automatic estimates of cell number are obtainable from extended depth of field images built from three-dimensional volumes of tissue (disector stacks). The automatic optical fractionator is more accurate, 100% objective and 8-10 times faster than the manual optical fractionator. An example of the automatic fractionator is provided for counts of immunostained neurons in neocortex of a genetically modified mouse model of neurodegeneration. Evidence is presented for the often overlooked prerequisite that accurate counting by the optical fractionator requires a thin focal plane generated by a high optical resolution lens.

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models.

A Hybrid Approach for Segmentation and Tracking of Myxococcus Xanthus Swarms

Cell segmentation and motion tracking in time-lapse images are fundamental problems in computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a type of rod-like cells with highly coordinated motion. The segmentation and tracking of M. xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult to identify individually in an accurate manner. The known cell tracking approaches mainly fall into two frameworks, detection association and model evolution, each having its own advantages and disadvantages. In this paper, we propose a new hybrid framework combining these two frameworks into one and leveraging their complementary advantages. Also, we propose an active contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid framework. Evaluated by 10 different datasets, our approach achieves considerable improvement over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and higher segmentation accuracy than performing segmentation in individual 2D images.

Deformable Graph Model for Tracking Epithelial Cell Sheets in Fluorescence Microscopy

We propose a novel method for tracking cells that are connected through a visible network of membrane junctions. Tissues of this form are common in epithelial cell sheets and resemble planar graphs where each face corresponds to a cell. We leverage this structure and develop a method to track the entire tissue as a deformable graph. This coupled model in which vertices inform the optimal placement of edges and vice versa captures global relationships between tissue components and leads to accurate and robust cell tracking. We compare the performance of our method with that of four reference tracking algorithms on four data sets that present unique tracking challenges. Our method exhibits consistently superior performance in tracking all cells accurately over all image frames, and is robust over a wide range of image intensity and cell shape profiles. This may be an important tool for characterizing tissues of this type especially in the field of developmental biology where automated cell analysis can help elucidate the mechanisms behind controlled cell-shape changes.

Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy

Red blood cell (RBC) phase images that are numerically reconstructed by digital holographic microscopy (DHM) can describe the cell structure and dynamics information beneficial for a quantitative analysis of RBCs. However, RBCs investigated with time-lapse DHM undergo temporal displacements when their membranes are loosely attached to the substrate during sedimentation on a glass surface or due to the microscope drift. Therefore, we need to develop a tracking algorithm to localize the same RBC among RBC image sequences and dynamically monitor its biophysical cell parameters; this information is helpful for studies on RBC-related diseases and drug tests. Here, we propose a method, which is a combination of the mean-shift algorithm and Kalman filter, to track a single RBC and demonstrate that the optical path length of the single RBC can be continually extracted from the tracked RBC. The Kalman filter is utilized to predict the target RBC position in the next frame. Then, the mean-shift algorithm starts execution from the predicted location, and a robust kernel, which is adaptive to changes in the RBC scale, shape, and direction, is designed to improve the accuracy of the tracking. Finally, the tracked RBC is segmented and parameters such as the RBC location are extracted to update the Kalman filter and the kernel function for mean-shift tracking; the characteristics of the target RBC are dynamically observed. Experimental results show the feasibility of the proposed algorithm.

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.

Accurate vessel segmentation with constrained B-snake

We describe an active contour framework with accurate shape and size constraints on the vessel cross-sectional planes to produce the vessel segmentation. It starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel boundary delineation on the cross-sectional planes derived from the extracted axis. The vessel boundary surface is deformed under constrained movements on the cross sections and is voxelized to produce the final vascular segmentation. The novelty of this paper lies in the accurate contour point detection of thin vessels based on the CT scanning model, in the efficient implementation of missing contour points in the problematic regions and in the active contour model with accurate shape and size constraints. The main advantage of our framework is that it avoids disconnected and incomplete segmentation of the vessels in the problematic regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic structure attached to a vessel), and thin vessels. It is particularly suitable for accurate segmentation of thin and low contrast vessels. Our method is evaluated and demonstrated on CT data sets from our partner site, and its results are compared with three related methods. Our method is also tested on two publicly available databases and its results are compared with the recently published method. The applicability of the proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic regions, is demonstrated with good results on both quantitative and qualitative experimentations; our segmentation algorithm can delineate vessel boundaries that have level of variability similar to those obtained manually.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.

Segmentation and Tracking of Lymphocytes Based on Modified Active Contour Models in Phase Contrast Microscopy Images

The paper proposes an improved active contour model for segmenting and tracking accurate boundaries of the single lymphocyte in phase-contrast microscopic images. Active contour models have been widely used in object segmentation and tracking. However, current external-force-inspired methods are weak at handling low-contrast edges and suffer from initialization sensitivity. In order to segment low-contrast boundaries, we combine the region information of the object, extracted by morphology gray-scale reconstruction, and the edge information, extracted by the Laplacian of Gaussian filter, to obtain an improved feature map to compute the external force field for the evolution of active contours. To alleviate initial location sensitivity, we set the initial contour close to the real boundaries by performing morphological image processing. The proposed method was tested on live lymphocyte images acquired through the phase-contrast microscope from the blood samples of mice, and comparative experimental results showed the advantages of the proposed method in terms of the accuracy and the speed. Tracking experiments showed that the proposed method can accurately segment and track lymphocyte boundaries in microscopic images over time even in the presence of low-contrast edges, which will provide a good prerequisite for the quantitative analysis of lymphocyte morphology and motility.

A robust algorithm for segmenting and tracking clustered cells in time-lapse fluorescent microscopy

We present herein a robust algorithm for cell tracking in a sequence of time-lapse 2-D fluorescent microscopy images. Tracking is performed automatically via a multiphase active contours algorithm adapted to the segmentation of clustered nuclei with obscure boundaries. An ellipse fitting method is applied to avoid problems typically associated with clustered, overlapping, or dying cells, and to obtain more accurate segmentation and tracking results. We provide quantitative validation of results obtained with this new algorithm by comparing them to the results obtained from the established CellProfiler, MTrack2 (plugin for Fiji), and LSetCellTracker software.

A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images

The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by nonstructured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the interframe cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study, a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase-contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy.

Fast and accurate automated cell boundary determination for fluorescence microscopy

Detailed measurement of cell phenotype information from digital fluorescence images has the potential to greatly advance biomedicine in various disciplines such as patient diagnostics or drug screening. Yet, the complexity of cell conformations presents a major barrier preventing effective determination of cell boundaries, and introduces measurement error that propagates throughout subsequent assessment of cellular parameters and statistical analysis. State-of-the-art image segmentation techniques that require user-interaction, prolonged computation time and specialized training cannot adequately provide the support for high content platforms, which often sacrifice resolution to foster the speedy collection of massive amounts of cellular data. This work introduces a strategy that allows us to rapidly obtain accurate cell boundaries from digital fluorescent images in an automated format. Hence, this new method has broad applicability to promote biotechnology.

A Cell Derived Active Contour (CDAC) method for robust tracking in low frame rate, low contrast phase microscopy - an example: the human hNT astrocyte

The problem of automated segmenting and tracking of the outlines of cells in microscope images is the subject of active research. While great progress has been made on recognizing cells that are of high contrast and of predictable shape, many situations arise in practice where these properties do not exist and thus many interesting potential studies - such as the migration patterns of astrocytes to scratch wounds - have been relegated to being largely qualitative in nature. Here we analyse a select number of recent developments in this area, and offer an algorithm based on parametric active contours and formulated by taking into account cell movement dynamics. This Cell-Derived Active Contour (CDAC) method is compared with two state-of-the-art segmentation methods for phase-contrast microscopy. Specifically, we tackle a very difficult segmentation problem: human astrocytes that are very large, thin, and irregularly-shaped. We demonstrate quantitatively better results for CDAC as compared to similar segmentation methods, and we also demonstrate the reliable segmentation of qualitatively different data sets that were not possible using existing methods. We believe this new method will enable new and improved automatic cell migration and movement studies to be made.

PombeX: robust cell segmentation for fission yeast transillumination images

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of a large amount of images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to aid in accurate detection of cell membrane and cytoplasm pixels, which is used to generate initial contours for cells. Gradient vector flow snake evolution is used to obtain the final cell contours. Finally, a machine learning-based validation of cell contours removes most incorrect or spurious contours. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of differences in image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods for yeast cells show that PombeX outperforms current methods, both in terms of segmentation accuracy and computational speed.

Red blood cell tracking using optical flow methods

The investigation of microcirculation is an important task in biomedical and physiological research because the microcirculation information, such as flow velocity and vessel density, is critical to monitor human conditions and develop effective therapies of some diseases. As one of the tasks of the microcirculation study, red blood cell (RBC) tracking presents an effective approach to estimate some parameters in microcirculation. The common method for RBC tracking is based on spatiotemporal image analysis, which requires the image to have high qualification and cells should have fixed velocity. Besides, for in vivo cell tracking, cells may disappear in some frames, image series may have spatial and temporal distortions, and vessel distribution can be complex, which increase the difficulties of RBC tracking. In this paper, we propose an optical flow method to track RBCs. It attempts to describe the local motion for each visible point in the frames using a local displacement vector field. We utilize it to calculate the displacement of a cell in two adjacent frames. Additionally, another optical flow-based method, scale invariant feature transform (SIFT) flow, is also presented. The experimental results show that optical flow is quite robust to the case where the velocity of cell is unstable, while SIFT flow works well when there is a large displacement of the cell between two adjacent frames. Our proposed methods outperform other methods when doing in vivo cell tracking, which can be used to estimate the blood flow directly and help to evaluate other parameters in microcirculation.

Segmentation and shape tracking of whole fluorescent cells based on the Chan-Vese model

We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.

A shape constrained parametric active contour model for breast contour detection

Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.

Image-based characterization of thrombus formation in time-lapse DIC microscopy

The characterization of thrombus formation in time-lapse DIC microscopy is of increased interest for identifying genes which account for atherothrombosis and coronary artery diseases (CADs). In particular, we are interested in large-scale studies on zebrafish, which result in large amount of data, and require automatic processing. In this work, we present an image-based solution for the automatized extraction of parameters quantifying the temporal development of thrombotic plugs. Our system is based on the joint segmentation of thrombotic and aortic regions over time. This task is made difficult by the low contrast and the high dynamic conditions observed in vivo DIC microscopic scenes. Our key idea is to perform this segmentation by distinguishing the different motion patterns in image time series rather than by solving standard image segmentation tasks in each image frame. Thus, we are able to compensate for the poor imaging conditions. We model motion patterns by energies based on the idea of dynamic textures, and regularize the model by two prior energies on the shape of the aortic region and on the topological relationship between the thrombus and the aorta. We demonstrate the performance of our segmentation algorithm by qualitative and quantitative experiments on synthetic examples as well as on real in vivo microscopic sequences.

Automatic abdominal fat assessment in obese mice using a segmental shape model

PURPOSE

To develop a computerized image analysis method to assess the quantity and distribution of abdominal fat tissues in an obese (ob/ob) mouse model relevant to 7 T magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A novel segmental shape model is presented that separates visceral adipose tissue (VAT) from subcutaneous adipose tissue (SAT). With shape and distance constraints, it deforms a contour inwards from the skin to the muscle wall and separates the connecting adipose tissues in an ob/ob mouse. The fat tissues are segmented by the adaptive fuzzy C means method to compensate for intensity variation in adipose images. The results were obtained by logical operations applied on the extracted fat images and the separated adipose masks.

RESULTS

The method was validated by manual segmentations on 109 axial slice images from 7 ob/ob mice. The average correlation coefficients of measured sizes between the automatic and manual results for total adipose tissue (TAT) is 0.907; SAT is 0.944; VAT is 0. 950. The average Dice coefficient of their positions for TAT is 0.941, SAT is 0.935, and VAT is 0.920.

CONCLUSION

The automated results correlate well with manual segmentations and the method can be used to increase laboratory automation.

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.

An optical counting technique with vertical hydrodynamic focusing for biological cells

A BARRIER IN SCALING LABORATORY PROCESSES INTO AUTOMATED MICROFLUIDIC DEVICES HAS BEEN THE TRANSFER OF LABORATORY BASED ASSAYS: Where engineering meets biological protocol. One basic requirement is to reliably and accurately know the distribution and number of biological cells being dispensed. In this study, a novel optical counting technique to efficiently quantify the number of cells flowing into a microtube is presented. REH, B-lymphoid precursor leukemia, are stained with a fluorescent dye and frames of moving cells are recorded using a charge coupled device (CCD) camera. The basic principle is to calculate the total fluorescence intensity of the image and to divide it by the average intensity of a single cell. This method allows counting the number of cells with an uncertainty +/-5%, which compares favorably to the standard biological methodology, based on the manual Trypan Blue assay, which is destructive to the cells and presents an uncertainty in the order of 20%. The use of a microdevice for vertical hydrodynamic focusing, which can reduce the background noise of out of focus cells by concentrating the cells in a thin layer, has further improved the technique. Computational fluid dynamics (CFD) simulation and confocal laser scanning microscopy images have shown an 82% reduction in the vertical displacement of the cells. For the flow rates imposed during this study, a throughput of 100-200 cellss is achieved.

Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.

Live cell tracking based on cellular state recognition from microscopic images

The analysis of cell motion is an essential process in fundamental medical studies because most active cellular functions involve motion. In this paper, a computer-assisted motion analysis system is proposed for cell tracking. In the proposed tracking process, unlike in conventional tracking methods, cellular states referring to the cellular life cycle are defined and appropriate strategies are adopted for cells at different states. The use of cellular state recognition allows detection of possible cell division and hence can improve the robustness of cell tracking. Experimental results show that cells can be successfully segmented and tracked over a long period of time, and the proposed system is found to be as accurate as manual tracking. Various quantitative analyses and visualizations are used to represent cell motion, which demonstrates the usefulness of the proposed system in the study of cell dynamics.

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.

Tracking cells in Life Cell Imaging videos using topological alignments

Background

With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells – many algorithms tend to recognize one cell as several cells or vice versa.

Results

We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program.

Conclusion

Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS).

Availability

The source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.

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

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

Automated 5-D analysis of cell migration and interaction in the thymic cortex from time-lapse sequences of 3-D multi-channel multi-photon images

This paper presents automated methods to quantify dynamic phenomena such as cell-cell interactions and cell migration patterns from time-lapse series of multi-channel three-dimensional image stacks of living specimens. Various 5-dimensional (x, y, z, t, lambda) images containing dendritic cells (DC), and T-cells or thymocytes in the developing mouse thymic cortex and lymph node were acquired by two-photon laser scanning microscopy (TPLSM). The cells were delineated automatically using a mean-shift clustering algorithm. This enables morphological measurements to be computed. A robust multiple-hypothesis tracking algorithm was used to track thymocytes (the DC were stationary). The tracking data enable dynamic measurements to be computed, including migratory patterns of thymocytes, and duration of thymocyte-DC contacts. Software was developed for efficient inspection, corrective editing, and validation of the automated analysis results. Our software-generated results agreed with manually generated measurements to within 8%.

Event-tracking model of adhesion identifies load-bearing bonds in rolling leukocytes

OBJECTIVES

P-selectin binding to P-selectin glycoprotein ligand-1 (PSGL)-1 mediates leukocyte rolling under conditions of inflammation and injury. The aims of this study were to develop an efficient, high temporal resolution model for direct simulation of leukocyte rolling and conduct a study of load-bearing bonds using the model.

MATERIALS AND METHODS

A stochastic pi-calculus-driven event-tracking model of adhesion (ETMA) was developed and compared with experimental data. Multiple simulations for each case were conducted to obtain high-confidence numerical characteristics of leukocyte rolling.

RESULTS

Leukocyte rolling and the underlying P-selectin-PSGL-1 bonds were studied under low wall shear rate (25-50 s(-1)) conditions from measured parameters of leukocyte rolling and bond properties. For the first time, the location, number, lifetime, history, and kinetics of load-bearing bonds and their influence on cell rolling were identified and instantaneous cell displacements, translational and rotational velocities, and cell-substrate distances derived. The model explains the commonly observed "stop-start" type rolling behavior and reveals that a few load-bearing bonds are sufficient to support rolling, while a large number of bonds dissociate before becoming load bearing.

CONCLUSIONS

ETMA provides a method for more precise, direct simulation of leukocyte rolling at low wall shear rates and sets a foundation upon which further refinements can be introduced.