A Robust Algorithm for Segmenting and Tracking Clustered Cells in Time-Lapse Fluorescent Microscopy

Tarnawski, Wojciech; Kurtcuoglu, Vartan; Lorek, Paweł; Bodych, Marcin; Rotter, Jerome I.; Muszkieta, Monika; Piwowar, L.; Poulikakos, Dimos; Majkowski, Michał; Ferrari, Aldo

doi:10.1109/jbhi.2013.2262233

Cited by 20 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a result of difficulty in computationally analyzing cell-ECM boundaries that are consistently changing as wound-edge cells polarize and migrate. While many software programs are capable of tracking fluorescently labeled cells [59][60][61][62][63][64] , the population of fluorescently labeled HUVECs in wound-edge migration experiments is approximately 30-40% of the total HUVEC population, and therefore the majority of the wound-edge cells must be visualized by phase-contrast or DIC imaging. Additionally, it is important when measuring wound-edge migration to evaluate fluorescent and non-fluorescent cells within the same wound in order to identify specific effects of expression constructs both within experimental study groups and between experimental study groups.…”

Section: Representative Resultsmentioning

confidence: 99%

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

Braun

Caesar

Dang

et al. 2016

JoVE

View full text Add to dashboard Cite

The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes within individual endothelial cells (ECs). These processes are critical for homeostatic maintenance such as wound healing, and are also crucial in promoting tumor growth and metastasis. EC morphology is defined by the organization of the cytoskeleton, a tightly regulated system of actin and microtubule (MT) dynamics that is known to control EC branching, polarity and directional migration, essential components of angiogenesis. To study MT dynamics, we used high-resolution fluorescence microscopy coupled with computational image analysis of fluorescently-labeled MT plus-ends to investigate MT growth dynamics and the regulation of EC branching morphology and directional migration. Time-lapse imaging of living Human Umbilical Vein Endothelial Cells (HUVECs) was performed following transfection with fluorescently-labeled MT End Binding protein 3 (EB3) and Mitotic Centromere Associated Kinesin (MCAK)-specific cDNA constructs to evaluate effects on MT dynamics. PlusTipTracker software was used to track EB3-labeled MT plus ends in order to measure MT growth speeds and MT growth lifetimes in time-lapse images. This methodology allows for the study of MT dynamics and the identification of how localized regulation of MT dynamics within sub-cellular regions contributes to the angiogenic processes of EC branching and migration.

show abstract

Section: Representative Resultsmentioning

confidence: 99%

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

Braun

Caesar

Dang

et al. 2016

JoVE

View full text Add to dashboard Cite

show abstract

“…The proposed three phase Chan Vese narrowband algorithm has been implemented to obtain the segmented images. The segmented images have been evaluated by using accuracy, precision, sensitivity and F-score [16] metrics. Table 3 and Table 4 compare the existing method and the proposed method in terms of the above mentioned metrics.…”

Section: Resultsmentioning

confidence: 99%

A Framework for Segmentation of Inhomogeneous Live Cell Images using Fractional Derivatives and Level Set Method

Kaur¹,

Sahambi²

2015

IJCA

View full text Add to dashboard Cite

Cell segmentation has gained significant importance in modern biological image processing applications. The commonly used image segmentation algorithms are region based and depend on the homogeneity of the intensities of the pixels in the region of interest. But due to the highly inhomogeneous behavior of cell nuclei and background, feature overlapping between the two regions lead to misclassification and poor segmentation results. This paper proposes a method to segment the cell images taking into consideration the intensity inhomogeneity issue. A fractional differential term has been introduced in the clustering criteria for bias correction for improving the homogeneity of the cell images. A method to optimize the fractional order for images has also been proposed. Further an improved narrow band level set method using Chan Vese model has been proposed to improve the computational speed of the algorithm. The proposed method is evaluated on datasets of 2D microscopy images and images with improved homogeneity have been obtained. The results also show improved segmentation results and the time efficient bahaviour of the proposed method.

show abstract

“…(Zhang, Wang, and Shi 2009), (Bergeest and Rohr 2012), (Tarnawski et al 2013), (Liao et al 2015), (Mouelhi et al 2013) Clustering These techniques are used in the first exploratory data analysis and to group patterns that are similar. Sometimes they are combined with other techniques.…”

Section: Segmentationmentioning

confidence: 99%

“…Authors in (Tarnawski et al 2013) propose a new algorithm for segmenting and tracking of clustered cells. The algorithm assumes that the objects to be tracked or segmented are of elliptical shape.…”

Section: Segmentationmentioning

confidence: 99%

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Gamarra

Zurek

San-Juan

2017

Journal of Information Systems Engineering & Management

View full text Add to dashboard Cite

Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

show abstract

A Robust Algorithm for Segmenting and Tracking Clustered Cells in Time-Lapse Fluorescent Microscopy

Cited by 20 publications

References 26 publications

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells

A Framework for Segmentation of Inhomogeneous Live Cell Images using Fractional Derivatives and Level Set Method

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Contact Info

Product

Resources

About