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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

3D soma detection.(A—B) Visualization of two representative image stacks of neuronal cultures. Note that the stacks have a very limited extension along the z direction where less that 10 μm are available, corresponding to about 25–30 optical slices. (C—D) The images illustrate, in red color, the detection of the somas from the confocal image stacks shown above.
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pone.0121886.g009: 3D soma detection.(A—B) Visualization of two representative image stacks of neuronal cultures. Note that the stacks have a very limited extension along the z direction where less that 10 μm are available, corresponding to about 25–30 optical slices. (C—D) The images illustrate, in red color, the detection of the somas from the confocal image stacks shown above.

Mentions: After having used the shearlet-based surface detection routine, we combined the regions the soma occupies in the images of the bottom half of the stack with the respective regions in the images of the top half of the stack to extract the entire volume. Fig. 9 shows some examples of soma extraction in 3D from confocal image stacks of neuronal cultures using the procedure described above. In each cases, the 3D image stacks consist of 512 × 512 × 25 voxels.


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

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

3D soma detection.(A—B) Visualization of two representative image stacks of neuronal cultures. Note that the stacks have a very limited extension along the z direction where less that 10 μm are available, corresponding to about 25–30 optical slices. (C—D) The images illustrate, in red color, the detection of the somas from the confocal image stacks shown above.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121886.g009: 3D soma detection.(A—B) Visualization of two representative image stacks of neuronal cultures. Note that the stacks have a very limited extension along the z direction where less that 10 μm are available, corresponding to about 25–30 optical slices. (C—D) The images illustrate, in red color, the detection of the somas from the confocal image stacks shown above.
Mentions: After having used the shearlet-based surface detection routine, we combined the regions the soma occupies in the images of the bottom half of the stack with the respective regions in the images of the top half of the stack to extract the entire volume. Fig. 9 shows some examples of soma extraction in 3D from confocal image stacks of neuronal cultures using the procedure described above. In each cases, the 3D image stacks consist of 512 × 512 × 25 voxels.

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