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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

Separation of clustered somas.The figure illustrates the application of the multiscale Directional Ratio in combination with the level set method to separate contiguous somas on the MIP of a confocal image of a neuronoal network culture. (A) Segmented image (detail). (B) Directional Ratio plot using directional filters of length 20 pixels (note that the diameter of a soma is about 40 pixels). (C) The blue region shows the points where the Directional Ratio exceeds the threshold 0.9, identifying the more isotropic region. (D) Directional Ratio plot using directional filters of length 40 pixels; to note that the larger values of the Directional Ratio are now concentrated within a smaller set inside the blob-like regions. (E) The blue region shows the points where the Directional Ratio in panel D exceeds the threshold 0.9, identifying the more isotropic region; note that contiguous somas are now split into two regions. (F) Soma detection obtained from the application of the level set method, using the initialization curves determined by the boundary of the initial soma region in (E).
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pone.0121886.g003: Separation of clustered somas.The figure illustrates the application of the multiscale Directional Ratio in combination with the level set method to separate contiguous somas on the MIP of a confocal image of a neuronoal network culture. (A) Segmented image (detail). (B) Directional Ratio plot using directional filters of length 20 pixels (note that the diameter of a soma is about 40 pixels). (C) The blue region shows the points where the Directional Ratio exceeds the threshold 0.9, identifying the more isotropic region. (D) Directional Ratio plot using directional filters of length 40 pixels; to note that the larger values of the Directional Ratio are now concentrated within a smaller set inside the blob-like regions. (E) The blue region shows the points where the Directional Ratio in panel D exceeds the threshold 0.9, identifying the more isotropic region; note that contiguous somas are now split into two regions. (F) Soma detection obtained from the application of the level set method, using the initialization curves determined by the boundary of the initial soma region in (E).

Mentions: The method described above to separate somas from neurites in the segmented images of neuronal cultures may inadvertently detect multiple contiguous somas as a single soma. To address this issue, we designed a refinement of the soma extraction routine that proceeds as follows. After running our soma extraction routine, if the resulting soma area is too large (according to a criterion described below), we re-compute the Directional Ratio at a coarser scale, that is, by changing the scale parameter j in such a way that the supports of the analyzing functions are longer. By measuring the strength of the directional coherence at a coarser scale, the application of a threshold on the Directional Ration will produce some smaller regions contained in the inner part of the segmented area. This is illustrated in Fig. 3. Next, similar to the above procedure, we apply the level set method by using the boundary curves of these inner regions as the initialization curves of the level set evolution equation. As the numerical test below will show, by propagating these curves until they touch each other, we are able to separate contiguous 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)

Separation of clustered somas.The figure illustrates the application of the multiscale Directional Ratio in combination with the level set method to separate contiguous somas on the MIP of a confocal image of a neuronoal network culture. (A) Segmented image (detail). (B) Directional Ratio plot using directional filters of length 20 pixels (note that the diameter of a soma is about 40 pixels). (C) The blue region shows the points where the Directional Ratio exceeds the threshold 0.9, identifying the more isotropic region. (D) Directional Ratio plot using directional filters of length 40 pixels; to note that the larger values of the Directional Ratio are now concentrated within a smaller set inside the blob-like regions. (E) The blue region shows the points where the Directional Ratio in panel D exceeds the threshold 0.9, identifying the more isotropic region; note that contiguous somas are now split into two regions. (F) Soma detection obtained from the application of the level set method, using the initialization curves determined by the boundary of the initial soma region in (E).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121886.g003: Separation of clustered somas.The figure illustrates the application of the multiscale Directional Ratio in combination with the level set method to separate contiguous somas on the MIP of a confocal image of a neuronoal network culture. (A) Segmented image (detail). (B) Directional Ratio plot using directional filters of length 20 pixels (note that the diameter of a soma is about 40 pixels). (C) The blue region shows the points where the Directional Ratio exceeds the threshold 0.9, identifying the more isotropic region. (D) Directional Ratio plot using directional filters of length 40 pixels; to note that the larger values of the Directional Ratio are now concentrated within a smaller set inside the blob-like regions. (E) The blue region shows the points where the Directional Ratio in panel D exceeds the threshold 0.9, identifying the more isotropic region; note that contiguous somas are now split into two regions. (F) Soma detection obtained from the application of the level set method, using the initialization curves determined by the boundary of the initial soma region in (E).
Mentions: The method described above to separate somas from neurites in the segmented images of neuronal cultures may inadvertently detect multiple contiguous somas as a single soma. To address this issue, we designed a refinement of the soma extraction routine that proceeds as follows. After running our soma extraction routine, if the resulting soma area is too large (according to a criterion described below), we re-compute the Directional Ratio at a coarser scale, that is, by changing the scale parameter j in such a way that the supports of the analyzing functions are longer. By measuring the strength of the directional coherence at a coarser scale, the application of a threshold on the Directional Ration will produce some smaller regions contained in the inner part of the segmented area. This is illustrated in Fig. 3. Next, similar to the above procedure, we apply the level set method by using the boundary curves of these inner regions as the initialization curves of the level set evolution equation. As the numerical test below will show, by propagating these curves until they touch each other, we are able to separate contiguous 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