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ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis.

Annila T, Lihavainen E, Marques IJ, Williams DR, Yli-Harja O, Ribeiro A - BMC Bioinformatics (2013)

Bottom Line: We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs.Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections.ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs.

Methods: We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface.

Results: We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression.

Conclusions: ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.

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Related in: MedlinePlus

Segmentation procedure. Top left: Image from the green fluorescence channel. Top right: Image filtered with LoG. Middle left: Result from LoG edge detection. Middle right: Result from Otsu's threshold. Bottom left: Segmented image after all steps, including morphological closing. Bottom right: Outline of the fish. For illustration purposes, the images are presented with inverted grayscale values.
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Figure 2: Segmentation procedure. Top left: Image from the green fluorescence channel. Top right: Image filtered with LoG. Middle left: Result from LoG edge detection. Middle right: Result from Otsu's threshold. Bottom left: Segmented image after all steps, including morphological closing. Bottom right: Outline of the fish. For illustration purposes, the images are presented with inverted grayscale values.

Mentions: To perform the registration, landmarks are extracted from the images. These are obtained from the outline of the fish, since, in zebrafish, different embryos have similar shapes and outlines when at the same developmental stage. For that, the outlines of the embryo are extracted by segmentation of the image obtained from either DIC or green fluorescence channel. This segmentation is performed in several steps. First, we threshold the image using Otsu's method [15]. Since weak edges are often mapped to zero by this method, in the next step, edge detection is used. For that, we find the zero-crossings of the image filtered by a Laplacian of Gaussian (LoG) with a standard deviation of 2 and a size of 13x13, as these settings were found to suppress noise while detecting edges in fine structures such as vasculature. The result is then combined with the result from Otsu's method by a binary OR-operation. We observed that, after this step, there may still exist small gaps and holes, due to low levels of fluorescence in some areas of the vasculature. Thus, morphological closing is applied. We use a disk-shaped structuring element with a radius of 25, which was found to be large enough to fill the gaps in the vasculature. Since the mask obtained likely contains small connected components resulting from noise, we find the fish by selecting the largest connected component in the mask. Finally, the outline of the fish is obtained by a boundary tracing algorithm [16]. The segmentation steps are exemplified in Figure 2 for fluorescence images, and in Figure 3 for DIC. In the latter, the colors are inverted.


ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis.

Annila T, Lihavainen E, Marques IJ, Williams DR, Yli-Harja O, Ribeiro A - BMC Bioinformatics (2013)

Segmentation procedure. Top left: Image from the green fluorescence channel. Top right: Image filtered with LoG. Middle left: Result from LoG edge detection. Middle right: Result from Otsu's threshold. Bottom left: Segmented image after all steps, including morphological closing. Bottom right: Outline of the fish. For illustration purposes, the images are presented with inverted grayscale values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750475&req=5

Figure 2: Segmentation procedure. Top left: Image from the green fluorescence channel. Top right: Image filtered with LoG. Middle left: Result from LoG edge detection. Middle right: Result from Otsu's threshold. Bottom left: Segmented image after all steps, including morphological closing. Bottom right: Outline of the fish. For illustration purposes, the images are presented with inverted grayscale values.
Mentions: To perform the registration, landmarks are extracted from the images. These are obtained from the outline of the fish, since, in zebrafish, different embryos have similar shapes and outlines when at the same developmental stage. For that, the outlines of the embryo are extracted by segmentation of the image obtained from either DIC or green fluorescence channel. This segmentation is performed in several steps. First, we threshold the image using Otsu's method [15]. Since weak edges are often mapped to zero by this method, in the next step, edge detection is used. For that, we find the zero-crossings of the image filtered by a Laplacian of Gaussian (LoG) with a standard deviation of 2 and a size of 13x13, as these settings were found to suppress noise while detecting edges in fine structures such as vasculature. The result is then combined with the result from Otsu's method by a binary OR-operation. We observed that, after this step, there may still exist small gaps and holes, due to low levels of fluorescence in some areas of the vasculature. Thus, morphological closing is applied. We use a disk-shaped structuring element with a radius of 25, which was found to be large enough to fill the gaps in the vasculature. Since the mask obtained likely contains small connected components resulting from noise, we find the fish by selecting the largest connected component in the mask. Finally, the outline of the fish is obtained by a boundary tracing algorithm [16]. The segmentation steps are exemplified in Figure 2 for fluorescence images, and in Figure 3 for DIC. In the latter, the colors are inverted.

Bottom Line: We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs.Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections.ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs.

Methods: We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface.

Results: We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression.

Conclusions: ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.

Show MeSH
Related in: MedlinePlus