Erik Meijering

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images

For the investigation of the molecular mechanisms involved in neurite outgrowth and differentiation, accurate and reproducible segmentation and quantification of neuronal processes are a prerequisite. To facilitate this task, we developed a semiautomatic neurite tracing technique. This article describes the design and validation of the technique.

Methods for cell and particle tracking.

Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images.

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Neuron tracing in perspective.

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases.

Quantitative evaluation of convolution-based methods for medical image interpolation.

Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sinc-approximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

Cell Segmentation: 50 Years Down the Road [Life Sciences]

Ever since the establishment of cell theory in the early 19th century, which recognized the cell as the fundamental building unit of life, biologists have sought to explain the underlying principles. Momentous discoveries were made over the course of many decades of research [1], but the quest to attain full understanding of cellular mechanisms and how to manipulate them to improve health continues to the present day, with bigger budgets, more minds, and more sophisticated tools than ever before. One of the tools to which a great deal of the progress in cell biology can be attributed is light microscopy [2]. The field has come a long way since Antoni van Leeuwenhoeks first steps in the 1670s toward improving and exploiting microscopic imaging for studying life at the cellular level. Not only do biologists today have a plethora of different, complementary microscopic imaging techniques at their disposal that enable them to visualize phenomena even way below the classical diffraction limit of light, advanced microscope systems also allow them to easily acquire very large numbers of images within just a matter of hours. The abundance, heterogeneity, dimensionality, and complexity of the data generated in modern imaging experiments rule out manual image management, processing, and analysis. Consequently, computerized techniques for performing these tasks have become of key importance for further progress in cell biology [3][6]. A central problem in many studies, and often regarded as the cornerstone of image analysis, is image segmentation. Specifically, since cellular morphology is an important phenotypic feature that is indicative of the physiological state of a cell, and since the cell contour is often required for subsequent analysis of intracellular processes (zooming in to nanoscale), or of cell sociology (zooming out to millimeter scale), the problem of cell segmentation has received increasing attention in past years [7]. Here we reflect on how the field has evolved over the years and how past developments can be expected to extrapolate into the future.

Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy

Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.

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.

In Vivo Characterization and Quantification of Atherosclerotic Carotid Plaque Components With Multidetector Computed Tomography and Histopathological Correlation

Objective—In a previous in vitro study we have demonstrated that atherosclerotic plaque components can be characterized with multidetector computed tomography (MDCT) based on differences in Hounsfield values (HV). Now we evaluated the use of MDCT in vivo to characterize and quantify atherosclerotic carotid plaque components compared with histology as reference standard. Methods and Results—Fifteen symptomatic patients with carotid stenosis (>70%) underwent MDCT angiography before carotid endarterectomy (CEA). From each CEA specimen 3 histological sections and corresponding MDCT images were selected. The HV of the major plaque components were assessed. The measured HV were: 657±416HU, 88±18HU, and 25±19HU for calcifications, fibrous tissue, and lipid core, respectively. The cut-off value to differentiate lipid core from fibrous tissue and fibrous tissue from calcifications was based on these measurements and set at 60 HU and 130 HU, respectively. Regression plots showed good correlations (R2>0.73) between MDCT and histology except for lipid core areas, which had a good correlation (R2=0.77) only in mildly calcified (0% to 10%) plaques. Conclusions—MDCT is able to quantify total plaque area, calcifications, and fibrous tissue in atherosclerotic carotid plaques in good correlation with histology. Lipid core can only be adequately quantified in mildly calcified plaques.

Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy

Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( ¿ 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.