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A probabilistic cell model in background corrected image sequences for single cell analysis.

Kachouie NN, Fieguth P, Jervis E - Biomed Eng Online (2010)

Bottom Line: The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably.The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada. nnezamod@mit.edu.

ABSTRACT

Background: Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame.

Methods: Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study). To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background.

Results: The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.

Conclusion: The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable of localizing single cells in microwells and can be adapted for the other cell types that may not have circular shape. This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

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Background estimation. (a) Two unprocessed blood stem cell images: (i) Frame 1. (ii) Frame 50. Scale bar is 20 μm. (b) Coarse cropped well: (i) Coarsely cropped well in which well boundaries are visible. (ii) Estimated background obtained by applying the point-wise method . (iii) Panel (i) after background subtraction.
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Figure 1: Background estimation. (a) Two unprocessed blood stem cell images: (i) Frame 1. (ii) Frame 50. Scale bar is 20 μm. (b) Coarse cropped well: (i) Coarsely cropped well in which well boundaries are visible. (ii) Estimated background obtained by applying the point-wise method . (iii) Panel (i) after background subtraction.

Mentions: Two original frames taken from a cropped well is depicted in Fig. 1(a-i) and 1(a-ii). Well cropping is often approximate and the well boundaries may be partially or completely visible in the cropped image sequence, as can be seen in Fig. 1(b-i). Modelling cells on a uniform, zero-mean background requires that any existing background be estimated and subtracted.


A probabilistic cell model in background corrected image sequences for single cell analysis.

Kachouie NN, Fieguth P, Jervis E - Biomed Eng Online (2010)

Background estimation. (a) Two unprocessed blood stem cell images: (i) Frame 1. (ii) Frame 50. Scale bar is 20 μm. (b) Coarse cropped well: (i) Coarsely cropped well in which well boundaries are visible. (ii) Estimated background obtained by applying the point-wise method . (iii) Panel (i) after background subtraction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967554&req=5

Figure 1: Background estimation. (a) Two unprocessed blood stem cell images: (i) Frame 1. (ii) Frame 50. Scale bar is 20 μm. (b) Coarse cropped well: (i) Coarsely cropped well in which well boundaries are visible. (ii) Estimated background obtained by applying the point-wise method . (iii) Panel (i) after background subtraction.
Mentions: Two original frames taken from a cropped well is depicted in Fig. 1(a-i) and 1(a-ii). Well cropping is often approximate and the well boundaries may be partially or completely visible in the cropped image sequence, as can be seen in Fig. 1(b-i). Modelling cells on a uniform, zero-mean background requires that any existing background be estimated and subtracted.

Bottom Line: The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably.The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada. nnezamod@mit.edu.

ABSTRACT

Background: Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame.

Methods: Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study). To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background.

Results: The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.

Conclusion: The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable of localizing single cells in microwells and can be adapted for the other cell types that may not have circular shape. This method can be potentially used for single cell analysis to study the temporal dynamics of cells.

Show MeSH