scholarly journals Cell Tracking using Coupled Active Surfaces for Nuclei and Membranes

2009 ◽  
Author(s):  
Kishore Mosaliganti ◽  
Benjamin Smith ◽  
Arnaud Gelas ◽  
Alexandre Gouaillard ◽  
Sean Megason
Keyword(s):  
Cell Tracking ◽  
Zebrafish Embryo ◽  
Active Contours ◽  
Time Lapse ◽  
Constant Intensity ◽  
Real Time Tracking ◽  
Tracking Filter ◽  
Microscopy Images ◽  
Processing Framework ◽  
Time Point

An Insight Toolkit (ITK) processing framework for segmenting and tracking nuclei in time-lapse microscopy images using coupled active contours is presented in this paper. We implement the method of Dufour et al.[2] to segment and track cells in fluorescence microscopy images. The basic idea is to model the image as a constant intensity background with constant intensity foreground components. We utilizes our earlier submissions on the Chan and Vese algorithm [1] and its multiphase extension [5] to build our new tracking filter. The tracking filter itk::MultiphaseLevelSetTracking inputs a segmentation result (or a coarse estimate) from the previous time-point along with the feature image and generates a new segmentation output. By iteratively repeating this process across all time-points, real-time tracking is made possible. We include 2D/3D example code, parameter settings and show the results generated on a 2D zebrafish embryo image series.

scholarly journals Level Set Segmentation: Active Contours without edge

2009 ◽  
Author(s):  
Kishore Mosaliganti ◽  
Benjamin Smith ◽  
Arnaud Gelas ◽  
Alexandre Gouaillard ◽  
sean megason
Keyword(s):  
Level Set ◽  
Level Sets ◽  
Active Contours ◽  
Level Set Methods ◽  
Constant Intensity ◽  
Piecewise Constant ◽  
Gradient Information ◽  
Cardiac Image ◽  
Contour Evolution ◽  
Processing Framework

An Insight Toolkit (ITK) processing framework for segmentation using active contours without edges is presented in this paper. Our algorithm is based on the work of Chan and Vese [1] that uses level- sets to accomplish region segmentation in images with poor or no gradient information. The basic idea is to partion the image into two piecewise constant intensity regions. This work is in contrast to the level-set methods currently available in ITK which necessarily require gradient information. Similar to those methods, the methods presented in this paper are also made efficient using a sparse implementation strategy that solves the contour evolution PDE at the level-set boundary. The framework consists of 6 new ITK filters that inherit in succession from itk::SegmentationFilter. We include 2D/3D example code, parameter settings and show the results generated on a 2D cardiac image.

scholarly journals Region-Based PDEs for Cells Counting and Segmentation in 3D+Time Images of Vertebrate Early Embryogenesis

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Barbara Rizzi ◽  
Alessandro Sarti
Keyword(s):  
Active Contour ◽  
Zebrafish Embryo ◽  
Active Contour Model ◽  
Real Data ◽  
Time Lapse ◽  
Cell Nuclei ◽  
Qualitative And Quantitative ◽  
Different Shapes ◽  
Zebrafish Embryogenesis ◽  
Microscopy Images

This paper is devoted to the segmentation of cell nuclei from time lapse confocal microscopy images, taken throughout early Zebrafish embryogenesis. The segmentation allows to identify and quantify the number of cells in the animal model. This kind of information is relevant to estimate important biological parameters such as the cell proliferation rate in time and space. Our approach is based on the active contour model without edges. We compare two different formulations of the model equation and evaluate their performances in segmenting nuclei of different shapes and sizes. Qualitative and quantitative comparisons are performed on both synthetic and real data, by means of suitable gold standard. The best approach is then applied on a number of time lapses for the segmentation and counting of cells during the development of a zebrafish embryo between the sphere and the shield stage.

scholarly journals A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

2018 ◽  
Author(s):  
Lamees Nasser ◽  
Thomas Boudier
Keyword(s):  
Fluorescence Microscopy ◽  
Cell Tracking ◽  
Time Lapse ◽  
Shape Variation ◽  
Denoising Method ◽  
Nuclei Segmentation ◽  
Cellular Processes ◽  
Challenging Tasks ◽  
Microscopy Images ◽  
Segmentation And Tracking

ABSTRACTTime-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes,i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithms, based on sparse coding, that can both enhance very faint and noisy nuclei but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters,with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96.96% recall for C.elegans dataset. Besides, in Drosophila dataset, our method achieved very high recall (99.3%).

scholarly journals Cell cycle inertia underlies a bifurcation in cell fates after DNA damage

2021 ◽  
Vol 7 (3) ◽  
pp. eabe3882
Author(s):  
Jenny F. Nathans ◽  
James A. Cornwell ◽  
Marwa M. Afifi ◽  
Debasish Paul ◽  
Steven D. Cappell
Keyword(s):  
Dna Damage ◽  
Dna Damage Response ◽  
Cell Tracking ◽  
Time Lapse ◽  
S Phase ◽  
Cdk Inhibitor ◽  
Cyclin Dependent Kinase ◽  
Cell Fates ◽  
Damage Response ◽  
Time Lapse Imaging

The G1-S checkpoint is thought to prevent cells with damaged DNA from entering S phase and replicating their DNA and efficiently arrests cells at the G1-S transition. Here, using time-lapse imaging and single-cell tracking, we instead find that DNA damage leads to highly variable and divergent fate outcomes. Contrary to the textbook model that cells arrest at the G1-S transition, cells triggering the DNA damage checkpoint in G1 phase route back to quiescence, and this cellular rerouting can be initiated at any point in G1 phase. Furthermore, we find that most of the cells receiving damage in G1 phase actually fail to arrest and proceed through the G1-S transition due to persistent cyclin-dependent kinase (CDK) activity in the interval between DNA damage and induction of the CDK inhibitor p21. These observations necessitate a revised model of DNA damage response in G1 phase and indicate that cells have a G1 checkpoint.

scholarly journals Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Cell Tracking ◽  
Time Lapse ◽  
Live Cell ◽  
Time Lapse Imaging
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