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Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cordelières FP, Petit V, Kumasaka M, Debeir O, Letort V, Gallagher SJ, Larue L - PLoS ONE (2013)

Bottom Line: Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U.Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences.Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

View Article: PubMed Central - PubMed

Affiliation: Institut Curie, CNRS UMR3348, plate-forme IBISA d'imagerie cellulaire et tissulaire, Orsay, France.

ABSTRACT
Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

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Illustration of the mean-shift model.A. Initialization of the generic kernel based on the user-defined position (x’0,y’0).The kernel (here an octagon, ndir = 8) is divided into sectors (S1 to S8), each one containing two nested triangle-shaped regions (Rb and Rw), one sensitive to dark and the other to bright pixels (“b” means “black” and “w” means “white”). Here, Rb6 and Rw6 are shown, with a total of 16 regions (Rb1 to Rb8 and Rw1 to Rw8). Only the contours of sectors 2-5 are shown in order to lighten and better visualize the figure. The cell is not presented for clarity.B. Adjustment of the position of the center at t0. Sixteen mass centers (gb1 to gb8 and gw1 to gw8) are first calculated from the intensities of the pixels from each region, (gb1 and gw1 are shown). Sector mass centers (C) are calculated from gwn and gbn, (C8 is shown as an example). The center (x0,y0) is defined as the centroid of the mass centers C1 to C8.C. Adaptation of the kernels to cell morphology at t0. The distances (di) between each mass center (gwn) and kernel center (x0,y0) are calculated (d8 is shown as an example). The new outer radii (rwn) are calculated based on dn, the average dn distances, the expansion factor and the anisotropy factor. rbn is assigned according to the ratio (rb / rw), which is initially defined by the user.D. Representation of the kernels at t1. Information is obtained applying the processes explained in B and C. The size of the sectors will increase or decrease (indicated by the arrows) as a function of cell shape modifications.
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pone-0081266-g001: Illustration of the mean-shift model.A. Initialization of the generic kernel based on the user-defined position (x’0,y’0).The kernel (here an octagon, ndir = 8) is divided into sectors (S1 to S8), each one containing two nested triangle-shaped regions (Rb and Rw), one sensitive to dark and the other to bright pixels (“b” means “black” and “w” means “white”). Here, Rb6 and Rw6 are shown, with a total of 16 regions (Rb1 to Rb8 and Rw1 to Rw8). Only the contours of sectors 2-5 are shown in order to lighten and better visualize the figure. The cell is not presented for clarity.B. Adjustment of the position of the center at t0. Sixteen mass centers (gb1 to gb8 and gw1 to gw8) are first calculated from the intensities of the pixels from each region, (gb1 and gw1 are shown). Sector mass centers (C) are calculated from gwn and gbn, (C8 is shown as an example). The center (x0,y0) is defined as the centroid of the mass centers C1 to C8.C. Adaptation of the kernels to cell morphology at t0. The distances (di) between each mass center (gwn) and kernel center (x0,y0) are calculated (d8 is shown as an example). The new outer radii (rwn) are calculated based on dn, the average dn distances, the expansion factor and the anisotropy factor. rbn is assigned according to the ratio (rb / rw), which is initially defined by the user.D. Representation of the kernels at t1. Information is obtained applying the processes explained in B and C. The size of the sectors will increase or decrease (indicated by the arrows) as a function of cell shape modifications.

Mentions: The algorithm approximates the cell border of each selected cell as a polygon, named region or kernel (Figure 1). This is composed of a defined number of isosceles triangles (‘sectors’) and the tips of all triangles correspond to the center of the cell. Each sector is composed of two nested isosceles triangles. The biggest triangle is attracted by bright pixels and the smallest triangle is attracted by dark pixels, generating novel kernels [11].


Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.

Cordelières FP, Petit V, Kumasaka M, Debeir O, Letort V, Gallagher SJ, Larue L - PLoS ONE (2013)

Illustration of the mean-shift model.A. Initialization of the generic kernel based on the user-defined position (x’0,y’0).The kernel (here an octagon, ndir = 8) is divided into sectors (S1 to S8), each one containing two nested triangle-shaped regions (Rb and Rw), one sensitive to dark and the other to bright pixels (“b” means “black” and “w” means “white”). Here, Rb6 and Rw6 are shown, with a total of 16 regions (Rb1 to Rb8 and Rw1 to Rw8). Only the contours of sectors 2-5 are shown in order to lighten and better visualize the figure. The cell is not presented for clarity.B. Adjustment of the position of the center at t0. Sixteen mass centers (gb1 to gb8 and gw1 to gw8) are first calculated from the intensities of the pixels from each region, (gb1 and gw1 are shown). Sector mass centers (C) are calculated from gwn and gbn, (C8 is shown as an example). The center (x0,y0) is defined as the centroid of the mass centers C1 to C8.C. Adaptation of the kernels to cell morphology at t0. The distances (di) between each mass center (gwn) and kernel center (x0,y0) are calculated (d8 is shown as an example). The new outer radii (rwn) are calculated based on dn, the average dn distances, the expansion factor and the anisotropy factor. rbn is assigned according to the ratio (rb / rw), which is initially defined by the user.D. Representation of the kernels at t1. Information is obtained applying the processes explained in B and C. The size of the sectors will increase or decrease (indicated by the arrows) as a function of cell shape modifications.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3842324&req=5

pone-0081266-g001: Illustration of the mean-shift model.A. Initialization of the generic kernel based on the user-defined position (x’0,y’0).The kernel (here an octagon, ndir = 8) is divided into sectors (S1 to S8), each one containing two nested triangle-shaped regions (Rb and Rw), one sensitive to dark and the other to bright pixels (“b” means “black” and “w” means “white”). Here, Rb6 and Rw6 are shown, with a total of 16 regions (Rb1 to Rb8 and Rw1 to Rw8). Only the contours of sectors 2-5 are shown in order to lighten and better visualize the figure. The cell is not presented for clarity.B. Adjustment of the position of the center at t0. Sixteen mass centers (gb1 to gb8 and gw1 to gw8) are first calculated from the intensities of the pixels from each region, (gb1 and gw1 are shown). Sector mass centers (C) are calculated from gwn and gbn, (C8 is shown as an example). The center (x0,y0) is defined as the centroid of the mass centers C1 to C8.C. Adaptation of the kernels to cell morphology at t0. The distances (di) between each mass center (gwn) and kernel center (x0,y0) are calculated (d8 is shown as an example). The new outer radii (rwn) are calculated based on dn, the average dn distances, the expansion factor and the anisotropy factor. rbn is assigned according to the ratio (rb / rw), which is initially defined by the user.D. Representation of the kernels at t1. Information is obtained applying the processes explained in B and C. The size of the sectors will increase or decrease (indicated by the arrows) as a function of cell shape modifications.
Mentions: The algorithm approximates the cell border of each selected cell as a polygon, named region or kernel (Figure 1). This is composed of a defined number of isosceles triangles (‘sectors’) and the tips of all triangles correspond to the center of the cell. Each sector is composed of two nested isosceles triangles. The biggest triangle is attracted by bright pixels and the smallest triangle is attracted by dark pixels, generating novel kernels [11].

Bottom Line: Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U.Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences.Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

View Article: PubMed Central - PubMed

Affiliation: Institut Curie, CNRS UMR3348, plate-forme IBISA d'imagerie cellulaire et tissulaire, Orsay, France.

ABSTRACT
Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.

Show MeSH
Related in: MedlinePlus