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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

Shearlet-based surface detection of a soma.The figure shows two views of the surface morphology reconstruction of a soma obtained by processing a confocal image stack with the shearlet surface detection routine.
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pone.0121886.g006: Shearlet-based surface detection of a soma.The figure shows two views of the surface morphology reconstruction of a soma obtained by processing a confocal image stack with the shearlet surface detection routine.

Mentions: The advantage of applying the surface detection routine within these smaller volumes rather than over the entire stack is that the run-time of the algorithm is significantly reduced despite the linear computational cost growth with the increase of volume size. Fig. 6 illustrates the application of our surface detection routine for the extraction of the upper soma region from confocal image stacks of neuronal cultures. The figures show that this approach is very efficient at detecting the upper section of the soma’s surface. However, we found that, due to the very low image contrast in the bottom part of the stack, the algorithm is unable to extract a complete surface boundary in the bottom section of the soma, even though the mask we found above tightly encloses the soma.


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

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

Shearlet-based surface detection of a soma.The figure shows two views of the surface morphology reconstruction of a soma obtained by processing a confocal image stack with the shearlet surface detection routine.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121886.g006: Shearlet-based surface detection of a soma.The figure shows two views of the surface morphology reconstruction of a soma obtained by processing a confocal image stack with the shearlet surface detection routine.
Mentions: The advantage of applying the surface detection routine within these smaller volumes rather than over the entire stack is that the run-time of the algorithm is significantly reduced despite the linear computational cost growth with the increase of volume size. Fig. 6 illustrates the application of our surface detection routine for the extraction of the upper soma region from confocal image stacks of neuronal cultures. The figures show that this approach is very efficient at detecting the upper section of the soma’s surface. However, we found that, due to the very low image contrast in the bottom part of the stack, the algorithm is unable to extract a complete surface boundary in the bottom section of the soma, even though the mask we found above tightly encloses the soma.

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