Intravital leukocyte detection using the gradient inverse coefficient of variation

Intravital microscopy for real-time monitoring of drug delivery and nanobiological processes

Intravital microscopy (IVM) expands our understanding of cellular and molecular processes, with applications ranging from fundamental biology to (patho)physiology and immunology, as well as from drug delivery to drug processing and drug efficacy testing. In this review, we highlight modalities, methods and model organisms that make up today's IVM landscape, and we present how IVM - via its high spatiotemporal resolution - enables analysis of metabolites, small molecules, nanoparticles, immune cells, and the (tumor) tissue microenvironment. We furthermore present examples of how IVM facilitates the elucidation of nanomedicine kinetics and targeting mechanisms, as well as of biological processes such as immune cell death, host-pathogen interactions, metabolic states, and disease progression. We conclude by discussing the prospects of IVM clinical translation and examining the integration of machine learning in future IVM practice.

An Efficient Microscale Technique for Determining the Erythrocyte Sedimentation Rate

The erythrocyte sedimentation rate (ESR) is a commonly used test to screen for inflammatory conditions such as infections, autoimmune diseases, and cancers. However, it is a bulk macroscale test that requires a relatively large blood sample and takes a long time to run. Moreover, it provides no information regarding cell sizes or interactions, which can be highly variable. To overcome these drawbacks, we developed a microfluidic microscopy-based protocol to dynamically track settling red blood cells (RBCs) to quantify velocity of cell settling, as a surrogate for the ESR. We imaged individual cells in a vertical microfluidic channel and applied a hybrid cell detection and tracking algorithm to compute settling velocities. We combined eigenvalue background subtraction and centroid detection together with the Kalman filter and Hungarian assignment solver algorithms to increase accuracy and computational speed. Our algorithm is designed to track settling RBCs/aggregates in high cellularity samples rather than single cells in suspension. Detection accuracy was 79.3%, which is comparable to state-of-the-art cell-tracking techniques. Compared with conventional ESR tests, our approach has the advantages of being automated, using microliter volumes of blood samples, and rapid turnaround.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Learning a cost function for microscope image segmentation

Quantitative analysis of microscopy images is increasingly important in clinical researchers' efforts to unravel the cellular and molecular determinants of disease, and for pathological analysis of tissue samples. Yet, manual segmentation and measurement of cells or other features in images remains the norm in many fields. We report on a new system that aims for robust and accurate semi-automated analysis of microscope images. A user interactively outlines one or more examples of a target object in a training image. We then learn a cost function for detecting more objects of the same type, either in the same or different images. The cost function is incorporated into an active contour model, which can efficiently determine optimal boundaries by dynamic programming. We validate our approach and compare it to some standard alternatives on three different types of microscopic images: light microscopy of blood cells, light microscopy of muscle tissue sections, and electron microscopy cross-sections of axons and their myelin sheaths.

Smart spotting of pulmonary TB cavities using CT images

One third of the world's population is thought to have been infected with mycobacterium tuberculosis (TB) with new infection occurring at a rate of about one per second. TB typically attacks the lungs. Indication of cavities in upper lobes of lungs shows the high infection. Traditionally, it has been detected manually by physicians. But the automatic technique proposed in this paper focuses on accurate detection of disease by computed tomography (CT) using computer-aided detection (CAD) system. The various steps of the detection process include the following: (i) image preprocessing, which is done by techniques such as resizing, masking, and Gaussian smoothening, (ii) image egmentation that is implemented by using mean-shift model and gradient vector flow (GVF) model, (iii) feature extraction that can be achieved by Gradient inverse coefficient of variation and circularity measure, and (iv) classification using Bayesian classifier. Experimental results show that its perfection of detecting cavities is very accurate in low false positive rate (FPR).

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.

Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior

The segmentation of thrombus and vessel in microscopic image sequences is of high interest for identifying genes linked to cardiovascular diseases. This task is however challenging because of the low contrast and the highly dynamic conditions observed in time-lapse DIC in-vivo microscopic scenes. In this work, we introduce a probabilistic framework for the joint segmentation of thrombus and vessel regions. Modeling the scene with dynamic textures, we derive two likelihood functions to account for both spatial and temporal discrepancies of the motion patterns. A tubular shape prior is moreover introduced to constrain the aortic region. Extensive experiments on microscopic sequences quantitatively show the good performance of our approach.

A hybrid knowledge-guided detection technique for screening of infectious pulmonary tuberculosis from chest radiographs

Tuberculosis (TB) is a deadly infectious disease and the presence of cavities in the upper lung zones is a strong indicator that the disease has developed into a highly infectious state. Currently, the detection of TB cavities is mainly conducted by clinicians observing chest radiographs. Diagnoses performed by radiologists are labor intensive and very often there is insufficient healthcare personnel available, especially in remote communities. After assessing existing approaches, we propose an automated segmentation technique which takes a hybrid knowledge-based Bayesian classification approach to detect TB cavities automatically. We apply gradient inverse coefficient of variation (GICOV) and circularity measures to classify detected features and confirm true TB cavities. By comparing with non hybrid approaches and the classical active contour techniques for feature extraction in medical images, experimental results demonstrate that our approach achieves high accuracy with a low false positive rate in detecting TB cavities.

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.

An affine transformation invariance approach to cell tracking

Accurate and robust methods for automatically tracking rolling leukocytes facilitate inflammation research as leukocyte motion is a primary indicator of inflammatory response in the microvasculature. This paper reports on an affine transformation invariance approach we proposed to track rolling leukocyte in intravital microscopy image sequences. The method is based on the affine transformation invariance property, which enables the accommodation of linear affine transformations (translation, rotation, and/or scaling) of the target, and a particle filter that overcomes the effect of image clutter. In our data set of 50 sequences, we compared the new approach with an active contour tracking method and a Monte Carlo tracker. With the manual tracking result provided by an operator as the reference, the root mean square errors for the active contour tracking method, the Monte Carlo tracker and the affine transformation invariance approach were 0.95 microm, 0.79 microm and 0.74 microm, respectively, and the percentage of frames tracked were 72%, 75% and 89%, respectively. The affine transformation invariance approach demonstrated more robust (being able to successfully locate target leukocyte in more frames) and more accurate (lower root mean square error) tracking performance. We also separately studied the ability of the affine transformation invariance approach to track a dark target leukocyte and a bright target leukocyte by using the number of frames tracked as an evaluation measure. Dark target leukocyte possesses similar image intensity to the background, making it difficult to be located. In 20 sequences where the target leukocyte was dark, the affine transformation invariance approach tracked more frames in 18 sequences and fewer frames in 2 sequences when compared with the active contour tracking method. In comparison with the Monte Carlo tracker, the affine invariance method tracked more frames in 9 sequences, the same number of frames in 7 sequences and fewer frames in 4 sequences. In tracking a bright target leukocyte in 30 sequences, the affine transformation invariance approach demonstrated superior performance in 7 sequences and inferior performance in 1 sequence when compared with the active contour tracking method. It outperformed the Monte Carlo tracker in 15 sequences and underperformed in 1 sequence.

Geometric observers for dynamically evolving curves

This paper proposes a deterministic observer framework for visual tracking based on non-parametric implicit (level-set) curve descriptions. The observer is continuous-discrete, with continuous-time system dynamics and discrete-time measurements. Its state-space consists of an estimated curve position augmented by additional states (e.g., velocities) associated with every point on the estimated curve. Multiple simulation models are proposed for state prediction. Measurements are performed through standard static segmentation algorithms and optical-flow computations. Special emphasis is given to the geometric formulation of the overall dynamical system. The discrete-time measurements lead to the problem of geometric curve interpolation and the discrete-time filtering of quantities propagated along with the estimated curve. Interpolation and filtering are intimately linked to the correspondence problem between curves. Correspondences are established by a Laplace-equation approach. The proposed scheme is implemented completely implicitly (by Eulerian numerical solutions of transport equations) and thus naturally allows for topological changes and subpixel accuracy on the computational grid.

Snakuscules

A snakuscule (a minuscule snake) is the simplest active contour that we were able to design while keeping the quintessence of traditional snakes: an energy term governed by the data, and a regularization term. Our construction is an area-based snake, as opposed to curve-based snakes. It is parameterized by just two points, thus further easing requirements on the optimizer. Despite their ultimate simplicity, snakuscules retain enough versatility to be employed for solving various problems such as cell counting and segmentation of approximately circular features. In this paper, we detail the design process of a snakuscule and illustrate its usefulness through practical examples. We claim that our didactic intentions are well served by the simplicity of snakuscules.

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.

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.