Joint Tracking of Cell Morphology and Motion (original) (raw)
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Biophysical Active Contours for Cell Tracking I: Tension and Bending
2006 International Conference on Image Processing, 2006
Automatic segmentation and tracking of biological objects from dynamic microscopy data is of great interest for quantitative biology. A successful framework for this task are active contours, curves that iteratively minimize a cost function, which contains both dataattachment terms and regularization constraints reflecting prior knowledge on the contour geometry. However the choice of these latter terms and of their weights is largely arbitrary, thus requiring timeconsuming empirical parameter tuning and leading to sub-optimal results. Here, we report on a first attempt to use regularization terms based on known biophysical properties of cellular membranes. The present study is restricted to 2D images and cells with a simple cytoskeletal cortex underlying the membrane. We describe our new active contour model and its implementation, and show a first application to real biological images. The obtained segmentation is slightly better than standard active contours, however the main advantage lies in the self-consistent and automated determination of the weights of regularization terms. This encouraging result will lead us to extend the approach to 3D and more complex cells.
Active Contours for Cell Tracking
2002
This paper introduces an active contour or snakebased method for tracking cells within a video sequence. Specifically, we apply our cell tracking techniques to rolling leukocytes observed in vivo (in living animal) from video microscopy. The analysis of leukocyte motion reveals cues about the mechanism of inflammatory disease. To attack the problem of tracking leukocytes in vivo, the proposed snake tracker utilizes shape and size information specific to the leukocytes. The principal contribution of this work lies in introducing the shape and size constraint as a geometric primitive in the parametric snake energy model. The energy functional is then minimized through the basic principles of the calculus of variations to obtain the Euler equations used in contour updating. We have developed a partial differential equation (PDE) based generalized gradient vector flow (GVF) that accommodates for contrast changes and weak cell edges. Whereas previous GVF models are sensitive to initial contour placement, the modified GVF construction with Dirichlet type boundary condition (BC) allows a snake tracker to be robust for a wide range of initial positions. Another contribution in this work is to incorporate an energy term in the snake model that eliminates the need for explicitly resampling the snake contour intermittently as performed in traditional snake evolution. Using animal experiments, we compare the accuracy of the proposed snake tracker with the correlation and centroid based tracker and show that the proposed tracker is superior in terms of increased number of frames tracked and reduced localization error.
A Robust Active Contour Approach for Studying Cell Deformation from Noisy Images
2009
This work presents a generalized formulation of the Snake model defining new terms for the internal and the external energy functionals. These modifications conjugate features of the object contour as well as the inside of the shape. The obtained model is significantly more accurate spatially on the image plane and temporally on the frame sequence. In particular, the application to single cell analysis is in focus: In this context, we show how to cast the specific problem into the extended framework we propose. Shape descriptors and suitable metrics are then derived from the curve representation. The boundary identification produced through the classic formulation shows a poor and imprecise segmentation and leads to misleading metrics. The new model instead represents the boundary and the derived shape parameters in a way more consistent with the visual perception of shape evolution and deformation.
Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
Cell boundary segmentation in live cell image sequences is the first step towards quantitative analysis of cell motion and behavior. The time lapse microscopy imaging produces large volumes of image sequence collections which requires fast and robust automatic segmentation of cell boundaries to utilize further automated tools such as cell tracking to quantify and classify cell behavior. This paper presents a methodology that is based on utilizing the temporal context of the cell image sequences to accurately delineate the boundaries of non-homogeneous cells. A novel flux tensor-based detection of moving cells provides initial localization that is further refined by a multi-feature level set-based method using an efficient additive operator splitting scheme. The segmentation result is processed by a watershed-based algorithm to avoid merging boundaries of neighboring cells. By utilizing robust features, the level-set algorithm produces accurate segmentation for non-homogeneous cells ...
Self-initialized active contours for microscopic cell image segmentation
Scientific Reports
Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complex...
INTELLIGENT ALGORITHMS FOR CELL TRACKING AND IMAGE SEGMENTATION
Sensitive and accurate cell tracking system is important to cell motility studies. Recently, researchers have developed several methods for detecting and tracking the living cells. To improve the living cells tracking systems performance and accuracy, we focused on developing a novel technique for image processing. The algorithm we propose presents novel image segmentation and tracking system technique to incorporate the advantages of both Topological Alignments and snakes for more accurate tracking approach. The results demonstrate that the proposed algorithm achieves accurate tracking for detecting and analyzing the mobility of the living cells. The RMSE between the manual and the computed displacement was less than 12% on average. Where the Active Contour method gave a velocity RMSE of less than 11%, improves to less than 8% by using the novel Algorithm. We have achieved better tracking and detecting for the cells, also the ability of the system to improve the low contrast, under and over segmentation which is the most cell tracking challenge problems and responsible for lacking accuracy in cell tracking techniques.
Image Segmentationand Intelligent Algorithms for Cell Tracking
Journal of Engineering Technology, 2016
Sensitive and accurate cell tracking system is important to cell motility studies. Recently, researchers have developed several methods for detecting and tracking the living cells. To improve the living cells tracking systems performance and accuracy, we focused on developing a novel technique for image processing. The algorithm we propose presents novel image segmentation and tracking system technique to incorporate the advantages of both Topological Alignments and snakes for more accurate tracking approach. The results demonstrate that the proposed algorithm achieves accurate tracking for detecting and analyzing the mobility of the living cells. The RMSE between the manual and the computed displacement was less than 12% on average. Where the Active Contour method gave a velocity RMSE of less than 11%, improves to less than 8% by using the novel Algorithm. We have achieved better tracking and detecting for the cells, also the ability of the system to improve the low contrast, under and over segmentation which is the most cell tracking challenge problems and responsible for lacking accuracy in cell tracking techniques.
Medical image analysis, 2018
We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested ...
PLoS ONE, 2013
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 phasecontrast 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.
Robust Tracking of Migrating Cells Using Four-Color Level Set Segmentation
Lecture Notes in Computer Science, 2006
Understanding behavior of migrating cells is becoming an emerging research area with many important applications. Segmentation and tracking constitute vital steps of this research. In this paper, we present an automated cell segmentation and tracking system designed to study migration of cells imaged with a phase contrast microscope. For segmentation the system uses active contour level set methods with a novel extension that efficiently prevents false-merge problem. Tracking is done by resolving frame to frame correspondences between multiple cells using a multi-distance, multi-hypothesis algorithm. Cells that move into the field-of-view, arise from cell division or disappear due to apoptosis are reliably segmented and tracked by the system. Robust tracking of cells, imaged with a phase contrast microscope is a challenging problem due to difficulties in segmenting dense clusters of cells. As cells being imaged have vague borders, close neighboring cells may appear to merge. These false-merges lead to incorrect trajectories being generated during the tracking process. Current level-set based approaches to solve the false-merge problem require a unique level set per object (the N-level set paradigm). The proposed approach uses evidence from previous frames and graph coloring principles and solves the same problem with only four level sets for any arbitrary number of similar objects, like cells.