Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos (original) (raw)
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Similarity-based motion tracking of cells in microscopic images
Studies in Health Technology and Informatics, 2009
Live-cell assays are used to study the dynamic functional cellular processes in high-content screening of drug discovery processes. The large amount of image data created during the screening requires automatic image-analysis procedures that can describe these dynamic processes. One class of tasks in this application is the tracking of cells and the description of the events and the changes in the cell characteristics so that the desired information can be extracted from it based on data-mining and knowledge-discovery methods. In this paper, we propose a similarity-based approach for motion detection of the entire cell. Results are given on a test series from a real drug discovery process.
IEEE Transactions on Biomedical Engineering, 2000
This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement; these problems become more acute when the cell population is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation, followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.
Joint Tracking of Cell Morphology and Motion
Lecture Notes in Computer Science, 2009
A new method is proposed for joint tracking of cell morphology and motion from 3D temporal cellular images. We adopt the framework of region-based active contours for segmentation, which is able to cope with objects having blurred boundaries. Motion estimation is performed by optical flow to increase the robustness and accuracy. Cell morphology and motion are modelled via a unified energy formulation and estimated iteratively searching for the minimum energy configuration. Experiments are carried out on synthetic and real cellular images to demonstrate the performance of the method.
Journal of Microscopy, 2009
Analysis of in vitro cell motility is a useful tool for assessing cellular response to a range of factors. However, the majority of cell-tracking systems available are designed primarily for use with fluorescently labelled images. In this paper, five commonly used tracking systems are examined for their performance compared with the use of a novel in-house celltracking system based on the principles of image registration and optical flow. Image registration is a tool commonly used in medical imaging to correct for the effects of patient motion during imaging procedures and works well on low-contrast images, such as those found in bright-field and phase-contrast microscopy. The five cell-tracking systems examined were Retrac, a manual tracking system used as the gold standard; CellTrack, a recently released freely downloadable software system that uses a combination of tracking methods; ImageJ, which is a freely available piece of software with a plug-in for automated tracking (MTrack2) and Imaris and Volocity, both commercially available automated tracking systems. All systems were used to track migration of human epithelial cells over ten frames of a phase-contrast time-lapse microscopy sequence. This showed that the in-house image-registration system was the most effective of those tested when tracking non-dividing epithelial cells in low-contrast images, with a successful tracking rate of 95%. The performance of the tracking systems was also evaluated by tracking fluorescently labelled epithelial cells imaged with both phase-contrast and confocal microscopy techniques. The results showed that using fluorescence microscopy instead of phase contrast does improve the tracking efficiency for each of the tested systems. For the in-house software, this improvement was relatively small (<5% difference in tracking success rate), whereas much greater improvements in performance were seen when using fluorescence microscopy with Volocity and ImageJ.
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 ...
Analysis of High-throughput Microscopy Videos: Catching Up with Cell Dynamics
Lecture Notes in Computer Science, 2015
We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.
Label-Free Mammalian Cell Tracking Enhanced by Precomputed Velocity Fields
2022
Label-free cell imaging, where the cell is not "labeled" or modified by fluorescent chemicals, is an important research area in the field of biology. It avoids altering the cell's properties which typically happens in the process of chemical labeling. However, without the contrast enhancement from the label, the analysis of label-free imaging is more challenging than label-based imaging. In addition, it provides few human interpretable features, and thus needs machine learning approaches to help with the identification and tracking of specific cells. We are interested in label-free phase contrast imaging to track cells flowing in a cell sorting device where images are acquired at 500 frames/s. Existing Multiple Object Tracking (MOT) methods face four major challenges when used for tracking cells in a microfluidic sorting device: (i) most of the cells have large displacements between frames without any overlap; (ii) it is difficult to distinguish between cells as they are visually similar to each other; (iii) the velocities of cells vary with the location in the device; (iv) the appearance of cells may change as they move in and out of the focal plane of the imaging sensor that observes the isolation process. In this paper, we introduce a method for tracking cells in a predefined flow in the sorting device via phase contrast microscopy. Our proposed method is based on DeepSORT and YOLOv4 and exploits prior knowledge of a cell's velocity to assist tracking. We modify the Kalman filter in DeepSORT to accommodate a non-constant velocity motion model and integrate a representative velocity field obtained from fluid dynamics into the Kalman filter. The experimental results show that our proposed method outperforms several MOT methods for tracking cells in the sorting device.
Automatic tracking of individual migrating cells using low-magnification dark-field microscopy
Journal of Microscopy, 2012
Many fundamental biological processes, such as the search for food, immunological responses and wound healing, depend on cell migration. Video microscopy allows the magnitude and direction of cell migration to be documented. Here, we present a simple and inexpensive method for simultaneous tracking of hundreds of migrating cells over periods of several days. Low-magnification dark-field microscopy was used to visualize individual cells whereas time-lapse video images were acquired by computer for future analysis. We employed an automated tracking algorithm to identify individual cells on each video image allowing migration paths to be tracked using a nearest neighbour algorithm. To test the method, we followed the time-course of migration of 3T3 fibroblasts, endothelial cells and individual amoeba in the absence of any chemical stimulus gradient. All cell types showed a 'random walk' behaviour in which mean squared displacement in position increased linearly with time. We defined a 'migration coefficient' (D mig ), analogous to a diffusion coefficient, which gave an estimate of cell migration rate. D mig depended on cell type and temperature. When amoebas were made to undergo chemotaxis, the cells no longer followed a random walk but instead moved at a near constant velocity (V av ) towards the chemotactic stimulus.
Detection of Biological Cells in Phase-Contrast Microscopy Images
2006 Fifth Mexican International Conference on Artificial Intelligence, 2006
In this paper, we propose an automatic method to obtain cells detection and cells migration tracking in order to analyze cells behaviors under different conditions. The images were obtained using phase-contrast video microscopy method. Proposed method normalizes original images in order to increase image contrast, and a classification process based on variance operator determines the nature of pixels in the image as cells or background. Each detected cell is associated to its centroid in order to initialize the tracking procedure to quantify the migration process. This technique is a fast way to describe cells migrations, robust to cell contracts and mitosis, all over their trajectories. * * M. Eng. Students
Tracking-Assisted Segmentation of Biological Cells
Cornell University - arXiv, 2019
U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex processes such as collision of cells, mitosis and apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking and propose to track individual nuclei over time. By modelling the behavioural pattern of the cells, we achieve improved segmentation and tracking performances through a re-segmentation procedure. Our preliminary investigations on the Fluo-N2DH-SIM+ and Fluo-N2DH-GOWT1 datasets demonstrate that absolute improvements of up to 3.8 % and 3.4% can be obtained in segmentation and tracking accuracy, respectively.