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FogBank: a single cell segmentation across multiple cell lines and image modalities.

Chalfoun J, Majurski M, Dima A, Stuelten C, Peskin A, Brady M - BMC Bioinformatics (2014)

Bottom Line: This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images.We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets.The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

View Article: PubMed Central - PubMed

Affiliation: Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA. joe.chalfoun@nist.gov.

ABSTRACT

Background: Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results: We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce. We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions: FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

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Related in: MedlinePlus

Geodesic distance illustration. A schematic figure to display the allocation of an unassigned pixel (x marked) to the closest seed point (yellow path) by means of the minimum geodesic distance between that pixel and the seed points in the image. The yellow path has a geodesic distance smaller than the orange or green path. The red pixels represent cell boundaries that cannot be traversed.
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Fig2: Geodesic distance illustration. A schematic figure to display the allocation of an unassigned pixel (x marked) to the closest seed point (yellow path) by means of the minimum geodesic distance between that pixel and the seed points in the image. The yellow path has a geodesic distance smaller than the orange or green path. The red pixels represent cell boundaries that cannot be traversed.

Mentions: The geodesic distance prevents pixels that are close to a cell but separated by a boundary from being assigned to that cell. Those pixels are instead assigned to a different cell that is further away in terms of number of pixels on the image, but closer in terms of geodesic distance as shown in FigureĀ 2.Figure 2


FogBank: a single cell segmentation across multiple cell lines and image modalities.

Chalfoun J, Majurski M, Dima A, Stuelten C, Peskin A, Brady M - BMC Bioinformatics (2014)

Geodesic distance illustration. A schematic figure to display the allocation of an unassigned pixel (x marked) to the closest seed point (yellow path) by means of the minimum geodesic distance between that pixel and the seed points in the image. The yellow path has a geodesic distance smaller than the orange or green path. The red pixels represent cell boundaries that cannot be traversed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4301455&req=5

Fig2: Geodesic distance illustration. A schematic figure to display the allocation of an unassigned pixel (x marked) to the closest seed point (yellow path) by means of the minimum geodesic distance between that pixel and the seed points in the image. The yellow path has a geodesic distance smaller than the orange or green path. The red pixels represent cell boundaries that cannot be traversed.
Mentions: The geodesic distance prevents pixels that are close to a cell but separated by a boundary from being assigned to that cell. Those pixels are instead assigned to a different cell that is further away in terms of number of pixels on the image, but closer in terms of geodesic distance as shown in FigureĀ 2.Figure 2

Bottom Line: This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images.We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets.The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

View Article: PubMed Central - PubMed

Affiliation: Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA. joe.chalfoun@nist.gov.

ABSTRACT

Background: Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results: We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce. We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions: FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Show MeSH
Related in: MedlinePlus