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A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Bioinformatics, 26, 2010, 8, S. 1091-1097 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| author_facet |
Qu, Lei Peng, Hanchuan Qu, Lei Peng, Hanchuan |
|---|---|
| author |
Qu, Lei Peng, Hanchuan |
| spellingShingle |
Qu, Lei Peng, Hanchuan Bioinformatics A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
qu, lei |
| spelling |
Qu, Lei Peng, Hanchuan 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btq072 <jats:title>Abstract</jats:title> <jats:p>Motivation: The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.</jats:p> <jats:p>Results: We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.</jats:p> <jats:p>Availability: The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton</jats:p> <jats:p>Contact: pengh@janelia.hhmi.org</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems Bioinformatics |
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| title |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_unstemmed |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_full |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_fullStr |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_full_unstemmed |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_short |
A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_sort |
a principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btq072 |
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2010 |
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1091-1097 |
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<jats:title>Abstract</jats:title>
<jats:p>Motivation: The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.</jats:p>
<jats:p>Results: We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.</jats:p>
<jats:p>Availability: The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton</jats:p>
<jats:p>Contact: pengh@janelia.hhmi.org</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| description | <jats:title>Abstract</jats:title> <jats:p>Motivation: The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.</jats:p> <jats:p>Results: We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.</jats:p> <jats:p>Availability: The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton</jats:p> <jats:p>Contact: pengh@janelia.hhmi.org</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| spelling | Qu, Lei Peng, Hanchuan 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btq072 <jats:title>Abstract</jats:title> <jats:p>Motivation: The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.</jats:p> <jats:p>Results: We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.</jats:p> <jats:p>Availability: The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton</jats:p> <jats:p>Contact: pengh@janelia.hhmi.org</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems Bioinformatics |
| spellingShingle | Qu, Lei, Peng, Hanchuan, Bioinformatics, A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_full | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_fullStr | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_full_unstemmed | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_short | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_sort | a principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| title_unstemmed | A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btq072 |