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Automated detection of soma location and morphology in neuronal network cultures.

Ozcan B, Negi P, Laezza F, Papadakis M, Labate D - PLoS ONE (2015)

Bottom Line: In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters.Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones.The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Mathematics, University of Houston, Houston, Texas, United States of America.

ABSTRACT
Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

No MeSH data available.


Related in: MedlinePlus

2D soma detection on large field-of-view image.Left: Tiled and stitched confocal fluorescent image (MIP view) of a neuronal culture. Image size = 1894 × 1894 pixels (1 pixel = 0.28 × 0.28μm). (B) Segmented binary image. Right: Soma detection and separation of contiguous somas. Segmented neurites are shown in red color. Detected somas are shown in light blue; in case of contiguous somas, the separated somas appear in green and orange colors.
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pone.0121886.g012: 2D soma detection on large field-of-view image.Left: Tiled and stitched confocal fluorescent image (MIP view) of a neuronal culture. Image size = 1894 × 1894 pixels (1 pixel = 0.28 × 0.28μm). (B) Segmented binary image. Right: Soma detection and separation of contiguous somas. Segmented neurites are shown in red color. Detected somas are shown in light blue; in case of contiguous somas, the separated somas appear in green and orange colors.

Mentions: To illustrate the capabilities of our approach on a larger field-of-view image, we applied our 2D soma detection algorithm on a tiled and stitched large field-of-view image of size 1894 × 1894 pixels containing about 45 neurons. The soma detection and segmentation result is reported in Fig. 12. The figure shows that our algorithm can reliably detect essentially all somas also in this larger image. Note that the soma detection algorithm is not expected to work for the somas overlapping the border of the image, as their shapes may be inconsistent with the model used by our method based on the Directional Ratio. Our method is also very effective in separating contiguous somas, even though it fails in some cases. About the center of the figure, three contiguous somas are correctly separated (shown in light blue, green and orange colors); however, in the top right region, where there is another set of three contiguous somas, the algorithm is only able to separate two of them. This is due to the fact two of the three soma are rather small and the area they occupy is not recognized as a location of multiple somas.


Automated detection of soma location and morphology in neuronal network cultures.

Ozcan B, Negi P, Laezza F, Papadakis M, Labate D - PLoS ONE (2015)

2D soma detection on large field-of-view image.Left: Tiled and stitched confocal fluorescent image (MIP view) of a neuronal culture. Image size = 1894 × 1894 pixels (1 pixel = 0.28 × 0.28μm). (B) Segmented binary image. Right: Soma detection and separation of contiguous somas. Segmented neurites are shown in red color. Detected somas are shown in light blue; in case of contiguous somas, the separated somas appear in green and orange colors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390318&req=5

pone.0121886.g012: 2D soma detection on large field-of-view image.Left: Tiled and stitched confocal fluorescent image (MIP view) of a neuronal culture. Image size = 1894 × 1894 pixels (1 pixel = 0.28 × 0.28μm). (B) Segmented binary image. Right: Soma detection and separation of contiguous somas. Segmented neurites are shown in red color. Detected somas are shown in light blue; in case of contiguous somas, the separated somas appear in green and orange colors.
Mentions: To illustrate the capabilities of our approach on a larger field-of-view image, we applied our 2D soma detection algorithm on a tiled and stitched large field-of-view image of size 1894 × 1894 pixels containing about 45 neurons. The soma detection and segmentation result is reported in Fig. 12. The figure shows that our algorithm can reliably detect essentially all somas also in this larger image. Note that the soma detection algorithm is not expected to work for the somas overlapping the border of the image, as their shapes may be inconsistent with the model used by our method based on the Directional Ratio. Our method is also very effective in separating contiguous somas, even though it fails in some cases. About the center of the figure, three contiguous somas are correctly separated (shown in light blue, green and orange colors); however, in the top right region, where there is another set of three contiguous somas, the algorithm is only able to separate two of them. This is due to the fact two of the three soma are rather small and the area they occupy is not recognized as a location of multiple somas.

Bottom Line: In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters.Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones.The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Mathematics, University of Houston, Houston, Texas, United States of America.

ABSTRACT
Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

No MeSH data available.


Related in: MedlinePlus