Cell segmentation, tracking, and mitosis detection using temporal context (original) (raw)
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Cellular tracking and mitosis detection in dense in-vitro cellular data
2012
Cell migration and cell division are two key processes that are associated with a wide range of biological phenomena including embryogenesis, inflammation, wound healing, tumour development etc. The study of these cellular processes has received a substantial interest from the cell and molecular scientists since the understanding of the mechanisms that stimulate and control these dynamic events has important practical implications. With the advent of modern microscopy imaging modalities the amount of information required to be analysed by the clinical experts has substantially increased and the development of computer-based automatic techniques that are able to robustly track cells in large image sequences is currently one of the most active topics of research. While cellular migration is the major source of information in describing biological processes, recent studies emphasised the growing importance of cell mitosis, as this information can be directly used in the estimation of t...
Joint level-set and spatio-temporal motion detection for cell segmentation
BMC Medical Genomics, 2016
Background: Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. Methods: In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. Results: We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. Conclusions: Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.
A System for Segmentation and Bi-Level Cell Tracking
International Journal of Bioscience, Biochemistry and Bioinformatics, 2012
Measurement of the proliferative behaviors of in vitro cells is important to many biomedical applications ranging from basic biological research to advanced applications, such as drug synthesis, stem cell manufacturing, and tissue engineering. The detection of borders within an image constitutes a process of digitalization of the image. Once the digitized image is obtained, the next step is the application of a specific process consisting in applying algorithms that allow the obtaining of raw data of the image. In this case, the applied algorithm to the digitized images was the Canny algorithm. This work presents a system to compute a vector representation for a selected cell of an image. The representation is in bi-level raster image.
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.
2013
The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by non-structured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the inter-frame cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy.
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
We propose a new algorithm to analyze cell migration. Sequences of frames are automatically recorded from standard (unmarked) cell cultures by means of phasecontrast microscopes equiped with video acquisition systems. This algorithm is able to automatically follow the locations in the reverse time of many cells during sequences covering relatively long periods of time such as 1 to 3 days. We then recombine the obtained cell tracks to detect mitoses and build a "mitotic tree". Several features are extract to characterize cell population motility and proliferation. As illustration the method is tested on U373 astrocytoma cell line.
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.
Advances in Intelligent Systems and Computing, 2017
Recent developments in live-cell microscopy imaging have led to the emergence of Single Cell Biology. This field has also been supported by the development of cell segmentation and tracking algorithms for data extraction. The validation of these algorithms requires benchmark databases, with manually labeled or artificially generated images, so that the ground truth is known. To generate realistic artificial images, we have developed a simulation platform capable of generating biologically inspired objects with various shapes and size, which are able to grow, divide, move and form specific clusters. Using this platform, we compared four tracking algorithms: Simple Nearest-Neighbor (NN), NN with Morphology (NNm) and two DBSCAN-based methodologies. We show that Simple NN performs well on objects with small velocities, while the others perform better for higher velocities and when objects form clusters. This platform for benchmark images generation and image analysis algorithms testing is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen\_v1.0.zip).
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.
An Automatic Overlap-Based Cell Tracking System
2010
In order to facilitate the extraction of quantitative data from live cell image sets, automated image analysis methods are needed. This paper presents an overlap-based cell tracking algorithm that has the ability to track cells across a set of time-lapse images based on the amount of overlap between cellular regions in consecutive frames. It uses the overlap to identify mitotic cells as well. This cell tracker is designed to be highly flexible, requires little user parameterization, and has a fast execution time. We demonstrate the performance of this tracker on NIH-3T3 mouse fibroblast cell line.