scispace - formally typeset
Proceedings ArticleDOI

Semi-automated tracking of muscle satellite cells in brightfield microscopy video

01 Sep 2012-pp 2825-2828
TL;DR: A semi-automated approach for satellite cell tracking on myofibers consisting of registration with illumination correction, background subtraction and particle filtering is proposed and initial experimental results show the effectiveness of the approach.
Abstract: Muscle satellite cells, also known as myogenic precursor cells, are the dedicated stem cells responsible for postnatal skeletal muscle growth, repair, and hypertrophy. Biological studies aimed at describing satellite cell activity on their host myofiber using timelapse light microscopy enable qualitative study, but high-throughput automatic tracking of satellite cells translocating on myofibers is very difficult due to their complex motion across the three-dimensional surface of myofibers and the lack of discriminating cell features. Other complicating factors include inhomogeneous illumination, fixed focal plane, low contrast, and stage motion. We propose a semi-automated approach for satellite cell tracking on myofibers consisting of registration with illumination correction, background subtraction and particle filtering. Initial experimental results show the effectiveness of the approach.
Citations
More filters

Journal ArticleDOI
TL;DR: A fast and accurate approach for automatic mitosis detection from histopathological images is proposed by restricting the scales with the maximization of relative-entropy between the cells and the background to result in precise cell segmentation.
Abstract: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40\times $ magnification.

46 citations


Additional excerpts

  • ...For background subtraction based cell detection, see [13]....

    [...]


Proceedings ArticleDOI
14 Dec 2014-
TL;DR: This work proposes a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees.
Abstract: Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an insight to the growth of the cancer and helps in developing individual treatment plans. The Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological slides. Pathologists manually identify mitotic figures from a few thousand slide images for each patient to determine the grade of the cancer. Mitotic figures are hard to identify as the appearance of the mitotic cells change at different phases of mitosis. So, the manual cancer grading is not only a tedious job but also prone to observer variability. We propose a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees. The random trees are assigned a tree penalty and a forest penalty depending on their classification performance at the training phase. The weight of a tree is calculated based on these penalties. The forest is trained through regeneration of population from weighted trees. The input data is classified based on weighted voting from the random trees after several populations. Experiments show at least 11 percent improvement in F1 score on more than 450 histopathological images at ×40 magnification.

7 citations


Additional excerpts

  • ...For background subtraction based cell detection, see [8]....

    [...]


Book ChapterDOI
TL;DR: Three protocols developed in the group for quantitatively analyzing satellite cell motility over time are described, which allow identification and longitudinal evaluation of individual cells over time and quantification of variations in motility due to intrinsic or extrinsic factors.
Abstract: Motility and/or chemotaxis of satellite cells has been suggested or observed in multiple in vitro and in vivo contexts. Satellite cell motility also affects the efficiency of muscle regeneration, particularly in the context of engrafted exogenous cells. Consequently, there is keen interest in determining what cell-autonomous and environmental factors influence satellite cell motility and chemotaxis in vitro and in vivo. In addition, the ability of activated satellite cells to relocate in vivo would suggest that they must be able to invade and transit through the extracellular matrix (ECM), which is supported by studies in which alteration or addition of matrix metalloprotease (MMP) activity enhanced the spread of engrafted satellite cells. However, despite its potential importance, analysis of satellite cell motility or invasion quantitatively even in an in vitro setting can be difficult; one of the most powerful techniques for overcoming these difficulties is timelapse microscopy. Identification and longitudinal evaluation of individual cells over time permits not only quantification of variations in motility due to intrinsic or extrinsic factors, it permits observation and analysis of other (frequently unsuspected) cellular activities as well. We describe here three protocols developed in our group for quantitatively analyzing satellite cell motility over time in two dimensions on purified ECM substrates, in three dimensions on a living myofiber, and in three dimensions through an artificial matrix.

1 citations


01 Jan 2014-
TL;DR: This article surveys the recent literature in the area of computer vision based automated cell tracking and discusses the latest trends and successes in the development and introduction of automated celltracking techniques and systems.
Abstract: With the advent of highly advanced optics and imaging system, currently biological research has reached a stage where scientists can study biological entities and processes at molecular and cellular-level in real time. However, a single experiment consists of hundreds and thousands of parameters to be recorded and a large population of microscopic objects to be tracked. Thus, making manual inspection of such events practically impossible. This calls for an approach to computer-vision based automated tracking and monitoring of cells in biological experiments. This technology promises to revolutionize the research in cellular biology and medical science which includes discovery of diseases by tracking the process in cells, development of therapy and drugs and the study of microscopic biological elements. This article surveys the recent literature in the area of computer vision based automated cell tracking. It discusses the latest trends and successes in the development and introduction of automated cell tracking techniques and systems.

References
More filters

Journal ArticleDOI
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

10,977 citations


4


"Semi-automated tracking of muscle s..." refers background in this paper

  • ...The motion of the cells being nonlinear, particle filter [14, 15, 16] is a justified choice to predict the position of the cells in a frame given the previous measurements....

    [...]


Journal ArticleDOI
TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.
Abstract: This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved.

6,465 citations


6


"Semi-automated tracking of muscle s..." refers methods in this paper

  • ...We now employ normalized 2D cross-correlation, a statistically robust measure of similarity, to register Sr,c with I1, The normalized 2D cross-correlation, denoted by NC1,(r,c), is given by [12]:...

    [...]


Journal ArticleDOI
TL;DR: A new algorithm based on a Monte Carlo method that can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low.
Abstract: A new algorithm for the prediction, filtering, and smoothing of non-Gaussian nonlinear state space models is shown. The algorithm is based on a Monte Carlo method in which successive prediction, filtering (and subsequently smoothing), conditional probability density functions are approximated by many of their realizations. The particular contribution of this algorithm is that it can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low. Several numerical examples are shown.

2,309 citations


"Semi-automated tracking of muscle s..." refers background in this paper

  • ...6 shows both the state equation and the measurement equation [17]....

    [...]


Book
01 Nov 1989-
Abstract: to the DLM: The First-Order Polynomial Model.- to the DLM: The Dynamic Regression Model.- The Dynamic Linear Model.- Univariate Time Series DLM Theory.- Model Specification and Design.- Polynomial Trend Models.- Seasonal Models.- Regression, Autoregression, and Related Models.- Illustrations and Extensions of Standard DLMs.- Intervention and Monitoring.- Multi-Process Models.- Non-Linear Dynamic Models: Analytic and Numerical Approximations.- Exponential Family Dynamic Models.- Simulation-Based Methods in Dynamic Models.- Multivariate Modelling and Forecasting.- Distribution Theory and Linear Algebra.

2,121 citations


Journal ArticleDOI
TL;DR: This review will highlight the origin and unique markers of the satellite cell population, the regulation by growth factors, and the response to physiological and pathological stimuli, and identify future research goals for the study of satellite cell biology.
Abstract: Adult skeletal muscle has a remarkable ability to regenerate following myotrauma. Because adult myofibers are terminally differentiated, the regeneration of skeletal muscle is largely dependent on a small population of resident cells termed satellite cells. Although this population of cells was identified 40 years ago, little is known regarding the molecular phenotype or regulation of the satellite cell. The use of cell culture techniques and transgenic animal models has improved our understanding of this unique cell population; however, the capacity and potential of these cells remain ill-defined. This review will highlight the origin and unique markers of the satellite cell population, the regulation by growth factors, and the response to physiological and pathological stimuli. We conclude by highlighting the potential therapeutic uses of satellite cells and identifying future research goals for the study of satellite cell biology.

1,585 citations


"Semi-automated tracking of muscle s..." refers background in this paper

  • ...Growth or repair of muscle involving the generation of new muscle cells requires the activity of a resident stem cell population, termed satellite cells [1]....

    [...]


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20171
20151
20142