Accumulative Difference Image Protocol for Particle Tracking in Fluorescence Microscopy Tested in Mouse Lymphonodes

The basic research in cell biology and in medical sciences makes large use of imaging tools mainly based on confocal fluorescence and, more recently, on non-linear excitation microscopy. Substantially the aim is the recognition of selected targets in the image and their tracking in time. We have developed a particle tracking algorithm optimized for low signal/noise images with a minimum set of requirements on the target size and with no a priori knowledge of the type of motion. The image segmentation, based on a combination of size sensitive filters, does not rely on edge detection and is tailored for targets acquired at low resolution as in most of the in-vivo studies. The particle tracking is performed by building, from a stack of Accumulative Difference Images, a single 2D image in which the motion of the whole set of the particles is coded in time by a color level. This algorithm, tested here on solid-lipid nanoparticles diffusing within cells and on lymphocytes diffusing in lymphonodes, appears to be particularly useful for the cellular and the in-vivo microscopy image processing in which few a priori assumption on the type, the extent and the variability of particle motions, can be done.     

Measuring handball players trajectories using an automatically trained boosting algorithm

The aim of the present paper is to propose and evaluate an automatically trained cascaded boosting detector algorithm based on morphological segmentation for tracking handball players. The proposed method was able to detect correctly 84% of players when applied to the second period of that same game used for training and 74% when applied to a different game. Furthermore, the analysis of the automatic training using boosting detector revealed general results such as the training time initially increased with the number of figures used, but as more figures were added, the training time decreased. Automatic morphological segmentation has shown to be a fast and efficient method for selecting image regions for the boosting detector and allowed an improvement in the automatic tracking of handball players.     

Smart Markers for Watershed-Based Cell Segmentation

Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of “smart markers” for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.     

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.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Switching characteristics of a model for biochemical-reaction networks describing autophosphorylation versus dephosphorylation of Ca2+/calmodulin-dependent protein kinase II

Ca2+/calmodulin-dependent protein kinase II (CaMKII) has been suggested to participate in various cellular phenomena triggered by Ca2+ signalling. In the present study, we addressed the functional role of CaMKII in molecular-signal transduction in cells by mathematical modelling of putative biochemical-reaction networks thought to represent an essential part of molecular events responsible for CaMKII-related cellular phenomena. These networks include Ca2+/calmodulin-dependent threonine-286/287 (Thr286/287) autophosphorylation of CaMKII versus dephosphorylation of the enzyme. Computer simulation of the model was performed to examine the relation between the Ca2+-signalling pattern as an input and the resulting degree of Thr286/287 autophosphorylation (m) as an output. Under the simplified condition that the Ca2+ concentration during Ca2+ signalling was set to remain constant with time, the biochemical-reaction networks were shown to function as a switch. There is a threshold for gamma, a parameter representing the probability that the Thr286/287-dephosphorylated CaMKII subunit binds with the Ca2+/calmodulin complex; if gamma is above this threshold, m increases with time to a large degree (switch-on); otherwise, it remains near zero (switch-off). Mathematically, this sharp onset of m at the threshold can be accounted for by a change in the structure of the dynamic system describing the model, from bistability to monostability; this is analogous to the first-order phase transition in statistical physics. For the oscillatory time course of [Ca2+], switching characteristics were also shown with respect to the frequency and the maximum amplitude of the oscillation. These results suggest that graded information mediated by Ca2+ signalling is digitized into all-or-non information mediated by Thr286/287 autophosphorylation of CaMKII.     

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.     

A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters

Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods.     

Instance segmentation and tracking of animals in wildlife videos: SWIFT - segmentation with filtering of tracklets

Reliably detecting and tracking animals in wildlife videos is an essential basis for researchers to be able to analyse animal behavior or recognize animal individuals. In order to correctly distinguish individual animals that are standing close to each other, bounding boxes around the animals are not sufficient. Instead, an exact contour of the animal, an instance mask, which is the result of an instance segmentation, is needed. In this paper, we present SWIFT, a novel multi-object tracking and segmentation (MOTS) pipeline that solves this task. We evaluate the functionality of our approach on a self-created wildlife video dataset containing red deer and fallow deer. Our dataset is one of the very few datasets in wildlife monitoring that is annotated with instance masks and tracking IDs. SWIFT significantly improves the quality of the instance masks compared to using a state-of-the-art instance segmentation approach from 0.432 average precision to 0.495 average precision. Our tracking algorithm uses multiple filtering steps to either delete tracks that are found incorrectly or to merge tracks that are not yet connected. This results in an increased multi-object tracking accuracy score in comparison to a state-of-the-art tracking approach from 57.2% to 63.8%, which means that our detected tracking results are less erroneous.     

Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

ObjectivesImage segmentation plays an important role in the analysis and understanding of the cellular process. However, this task becomes difficult when there is intensity inhomogeneity between regions, and it is more challenging in the presence of the noise and clustered cells. The goal of the paper is propose an image segmentation framework that tackles the above cited problems.Material and methodsA new method composed of two steps is proposed: First, segment the image using B-spline level set with Region-Scalable Fitting (RSF) active contour model, second apply the Watershed algorithm based on new object markers to refine the segmentation and separate clustered cells. The major contributions of the paper are: 1) Use of a continuous formulation of the level set in the B-spline basis, 2) Develop the energy function and its derivative by introducing the RSF model to deal with intensity inhomogeneity, 3) For the Watershed, propose a relevant choice of markers that considers the cell properties.ResultsExperimental results are performed on widely used synthetic images, in addition to simulated and real biological images, without and with additive noise. They attest the high quality of segmentation of the proposed method in terms of quantitative and qualitative evaluation.ConclusionThe proposed method is able to tackle many difficulties at the same time: overlapped intensities, noise, different cell sizes and clustered cells. It provides an efficient tool for image segmentation especially biological ones.     

Automatic Region-Based Brain Classification of MRI-T1 Data

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.     

SuperSegger: robust image segmentation,analysis and lineage tracking of bacterial cells

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame‐to‐frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB‐based image processing package well‐suited to quantitative analysis of high‐throughput live‐cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine‐learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame‐to‐frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell‐cycle dynamics in bacteria as well as cell‐contact mediated phenomena. This package has a range of built‐in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.     

Lateral Motion and Bending of Microtubules Studied with a New Single-Filament Tracking Routine in Living Cells

The cytoskeleton is involved in numerous cellular processes such as migration, division, and contraction and provides the tracks for transport driven by molecular motors. Therefore, it is very important to quantify the mechanical behavior of the cytoskeletal filaments to get a better insight into cell mechanics and organization. It has been demonstrated that relevant mechanical properties of microtubules can be extracted from the analysis of their motion and shape fluctuations. However, tracking individual filaments in living cells is extremely complex due, for example, to the high and heterogeneous background. We introduce a believed new tracking algorithm that allows recovering the coordinates of fluorescent microtubules with ∼9 nm precision in in vitro conditions. To illustrate potential applications of this algorithm, we studied the curvature distributions of fluorescent microtubules in living cells. By performing a Fourier analysis of the microtubule shapes, we found that the curvatures followed a thermal-like distribution as previously reported with an effective persistence length of ∼20 μm, a value significantly smaller than that measured in vitro. We also verified that the microtubule-associated protein XTP or the depolymerization of the actin network do not affect this value; however, the disruption of intermediate filaments decreased the persistence length. Also, we recovered trajectories of microtubule segments in actin or intermediate filament-depleted cells, and observed a significant increase of their motion with respect to untreated cells showing that these filaments contribute to the overall organization of the microtubule network. Moreover, the analysis of trajectories of microtubule segments in untreated cells showed that these filaments presented a slower but more directional motion in the cortex with respect to the perinuclear region, and suggests that the tracking routine would allow mapping the microtubule dynamical organization in cells.     

Adaptive color basis transformation. An aid in image segmentation

Multispectral images of stained cells enable the use of color differences to segment and/or to discriminate between image components, such as cell types and cellular subcomponents. When the spectral characteristics of the image components do not change over the area of a slide or from slide to slide, one can create a constant weighted linear combination of spectral images to generate one-dimensional or two-dimensional images that have the desired contrast between the image components that must be discriminated. However, when the spectral characteristics are not constant, i.e., when they vary from image to image, a constant weighted linear combination cannot be employed; instead, an appropriate solution must be found for each selected image. This is usually a time-consuming, manual procedure that cannot be employed in a fully automated process of discriminating and segmenting stained cells. This paper describes an algorithm that uses principal components decomposition basis vectors to generate a nonstatic weighted linear combination of color images that can be used by an automated system. This algorithm relies on a semiconstant relationship between the areas (sizes) of the image components that are to be discriminated and/or segmented. The technique has been successfully applied as an aid in the segmentation of images of stained cervical smears; the images were acquired with a three-chip CCD camera that generates three broad-band color images.     

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.     

Holomeric vs. meromeric segmentation: a tale of centipedes, leeches, and rhombomeres

SUMMARY Explaining the origin and evolution of segmentation is central to understanding the body plan of major animal groups such as arthropods, annelids, and vertebrates. One major shortcoming of current views on segmentation is the failure to recognize the existence of two layers of segmentation. I distinguish here holomeric segmentation, involving the whole body axis (or the whole axis of an appendage) and producing " true" segments (eosegments); and meromeric segmentation, producing merosegments within one or more eosegment(s). In terms of developmental mechanisms, meromeric segmentation is probably the same as compartmentalization. This process follows two rules: (1) merosegments are formed from a stereotyped pattern of subdivisions, where only the merosegments in contact to the anterior or posterior boundary of the eosegment are allowed to divide; (2) contiguous eosegments undergoing meromeric segmentation generate merosegments according to identical lineage patterns apart from possible lineage truncation in one or a few terminal eosegments. The segmentation model proposed in this paper is mainly supported by evidence from comparative morphology, but it is compatible with known cellular and developmental mechanisms. The development of vertebrate rhombomeres, the annulation of leeches, the subdivision of the distal part of insect antenna into flagellomeres and the segmentation of centipedes are interpreted here in terms of meromeric segmentation. Some of these phenomena, like centipede segmentation, have thus far defied all attempts at an explanation, both in mechanistic (developmental) and phylogenetic terms. The model presented in this paper suggests a rich research agenda at all levels, from molecular and genetic to morphological and phylogenetic.