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A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D.

Santella A, Du Z, Nowotschin S, Hadjantonakis AK, Bao Z - BMC Bioinformatics (2010)

Bottom Line: Because our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches.Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain.As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.

ABSTRACT

Background: To exploit the flood of data from advances in high throughput imaging of optically sectioned nuclei, image analysis methods need to correctly detect thousands of nuclei, ideally in real time. Variability in nuclear appearance and undersampled volumetric data make this a challenge.

Results: We present a novel 3D nuclear identification method, which subdivides the problem, first segmenting nuclear slices within each 2D image plane, then using a shape model to assemble these slices into 3D nuclei. This hybrid 2D/3D approach allows accurate accounting for nuclear shape but exploits the clear 2D nuclear boundaries that are present in sectional slices to avoid the computational burden of fitting a complex shape model to volume data. When tested over C. elegans, Drosophila, zebrafish and mouse data, our method yielded 0 to 3.7% error, up to six times more accurate as well as being 30 times faster than published performances. We demonstrate our method's potential by reconstructing the morphogenesis of the C. elegans pharynx. This is an important and much studied developmental process that could not previously be followed at this single cell level of detail.

Conclusions: Because our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches. Both of these characteristics are necessary for practical analysis of overwhelmingly large data sets where processing must be scalable to hundreds of thousands of cells and where the time cost of manual error correction makes it impossible to use data with high error rates. Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain. As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

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Slice extraction and nuclear definition. a. An x,y plane through C. elegans volume data at the ~350 cell stage. b. the corresponding slice through the 3D DoG filtered volume. c. Slices are segmented by casting out rays in search of a zero crossing. The 2D intensity maxima where rays originate are marked as black dots. Final end points of search rays are marked as blue dots. These points define a polygonal slice; multiple slices can be assembled together to yield a 3D nuclear boundary. d. Nuclear shape definition. The position, intensity, and size of each slice that might be part of a nucleus are measured relative to the nuclear center, and also relative to the closest slice between the possible member and the nuclear center. These measurements make up the 7D vector that represents a slice and nucleus center pairing. Actual nuclear extraction starts from the center and in turn considers the likelihood of each slice as an endpoint for the nucleus.
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Figure 1: Slice extraction and nuclear definition. a. An x,y plane through C. elegans volume data at the ~350 cell stage. b. the corresponding slice through the 3D DoG filtered volume. c. Slices are segmented by casting out rays in search of a zero crossing. The 2D intensity maxima where rays originate are marked as black dots. Final end points of search rays are marked as blue dots. These points define a polygonal slice; multiple slices can be assembled together to yield a 3D nuclear boundary. d. Nuclear shape definition. The position, intensity, and size of each slice that might be part of a nucleus are measured relative to the nuclear center, and also relative to the closest slice between the possible member and the nuclear center. These measurements make up the 7D vector that represents a slice and nucleus center pairing. Actual nuclear extraction starts from the center and in turn considers the likelihood of each slice as an endpoint for the nucleus.

Mentions: Given that image planes are widely spaced along the z axis, adjacent and similar voxels along the z axis are fairly likely to belong to separate nuclei, frustrating naive detection and segmentation methods. A shape model is necessary to guide segmentation, filling in boundary information that is not locally available in the image. Our method views nuclei as a collection of slices and uses a shape model that consists of expectations about slice size, brightness and location relative to other slices (See Figure 1 for a graphical overview of this process from image data to segmented nucleus).


A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D.

Santella A, Du Z, Nowotschin S, Hadjantonakis AK, Bao Z - BMC Bioinformatics (2010)

Slice extraction and nuclear definition. a. An x,y plane through C. elegans volume data at the ~350 cell stage. b. the corresponding slice through the 3D DoG filtered volume. c. Slices are segmented by casting out rays in search of a zero crossing. The 2D intensity maxima where rays originate are marked as black dots. Final end points of search rays are marked as blue dots. These points define a polygonal slice; multiple slices can be assembled together to yield a 3D nuclear boundary. d. Nuclear shape definition. The position, intensity, and size of each slice that might be part of a nucleus are measured relative to the nuclear center, and also relative to the closest slice between the possible member and the nuclear center. These measurements make up the 7D vector that represents a slice and nucleus center pairing. Actual nuclear extraction starts from the center and in turn considers the likelihood of each slice as an endpoint for the nucleus.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3008706&req=5

Figure 1: Slice extraction and nuclear definition. a. An x,y plane through C. elegans volume data at the ~350 cell stage. b. the corresponding slice through the 3D DoG filtered volume. c. Slices are segmented by casting out rays in search of a zero crossing. The 2D intensity maxima where rays originate are marked as black dots. Final end points of search rays are marked as blue dots. These points define a polygonal slice; multiple slices can be assembled together to yield a 3D nuclear boundary. d. Nuclear shape definition. The position, intensity, and size of each slice that might be part of a nucleus are measured relative to the nuclear center, and also relative to the closest slice between the possible member and the nuclear center. These measurements make up the 7D vector that represents a slice and nucleus center pairing. Actual nuclear extraction starts from the center and in turn considers the likelihood of each slice as an endpoint for the nucleus.
Mentions: Given that image planes are widely spaced along the z axis, adjacent and similar voxels along the z axis are fairly likely to belong to separate nuclei, frustrating naive detection and segmentation methods. A shape model is necessary to guide segmentation, filling in boundary information that is not locally available in the image. Our method views nuclei as a collection of slices and uses a shape model that consists of expectations about slice size, brightness and location relative to other slices (See Figure 1 for a graphical overview of this process from image data to segmented nucleus).

Bottom Line: Because our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches.Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain.As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.

ABSTRACT

Background: To exploit the flood of data from advances in high throughput imaging of optically sectioned nuclei, image analysis methods need to correctly detect thousands of nuclei, ideally in real time. Variability in nuclear appearance and undersampled volumetric data make this a challenge.

Results: We present a novel 3D nuclear identification method, which subdivides the problem, first segmenting nuclear slices within each 2D image plane, then using a shape model to assemble these slices into 3D nuclei. This hybrid 2D/3D approach allows accurate accounting for nuclear shape but exploits the clear 2D nuclear boundaries that are present in sectional slices to avoid the computational burden of fitting a complex shape model to volume data. When tested over C. elegans, Drosophila, zebrafish and mouse data, our method yielded 0 to 3.7% error, up to six times more accurate as well as being 30 times faster than published performances. We demonstrate our method's potential by reconstructing the morphogenesis of the C. elegans pharynx. This is an important and much studied developmental process that could not previously be followed at this single cell level of detail.

Conclusions: Because our approach is specialized for the characteristics of optically sectioned nuclear images, it can achieve superior accuracy in significantly less time than other approaches. Both of these characteristics are necessary for practical analysis of overwhelmingly large data sets where processing must be scalable to hundreds of thousands of cells and where the time cost of manual error correction makes it impossible to use data with high error rates. Our approach is fast, accurate, available as open source software and its learned shape model is easy to retrain. As our pharynx development example shows, these characteristics make single cell analysis relatively easy and will enable novel experimental methods utilizing complex data sets.

Show MeSH