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Automatic 3D neuron tracing using all-path pruning.

Peng H, Long F, Myers G - Bioinformatics (2011)

Bottom Line: To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image.We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).The software is available upon request.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janella.hhmi.org

ABSTRACT

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d.

Contact: pengh@janelia.hhmi.org.

Show MeSH
Two reconstructions produced by APP (green) and V3D-Neuron 1.0's 1-point-to-N-point automatic tracing function (red). The contours of the spherical structural components of reconstruction nodes are overlaid on top of the original image (gray scale).
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Figure 8: Two reconstructions produced by APP (green) and V3D-Neuron 1.0's 1-point-to-N-point automatic tracing function (red). The contours of the spherical structural components of reconstruction nodes are overlaid on top of the original image (gray scale).

Mentions: With the prior ending points available, V3D-Neuron 1.0's 1-point-to-N-point tracing is a powerful semi-automatic method to reconstruct neuron morphology. For the datasets used in the previous sections, we compared the fully automatic APP with V3D-Neuron 1.0, using the same seed location. Figure 8 shows that both methods produce very similar skeletons (SD=0.84 voxels, SSD=3.55 voxels, SSD%=7.6%). This means APP is as good as the semi-automatic method, without any manual guidance.Fig. 8.


Automatic 3D neuron tracing using all-path pruning.

Peng H, Long F, Myers G - Bioinformatics (2011)

Two reconstructions produced by APP (green) and V3D-Neuron 1.0's 1-point-to-N-point automatic tracing function (red). The contours of the spherical structural components of reconstruction nodes are overlaid on top of the original image (gray scale).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117353&req=5

Figure 8: Two reconstructions produced by APP (green) and V3D-Neuron 1.0's 1-point-to-N-point automatic tracing function (red). The contours of the spherical structural components of reconstruction nodes are overlaid on top of the original image (gray scale).
Mentions: With the prior ending points available, V3D-Neuron 1.0's 1-point-to-N-point tracing is a powerful semi-automatic method to reconstruct neuron morphology. For the datasets used in the previous sections, we compared the fully automatic APP with V3D-Neuron 1.0, using the same seed location. Figure 8 shows that both methods produce very similar skeletons (SD=0.84 voxels, SSD=3.55 voxels, SSD%=7.6%). This means APP is as good as the semi-automatic method, without any manual guidance.Fig. 8.

Bottom Line: To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image.We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).The software is available upon request.

View Article: PubMed Central - PubMed

Affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janella.hhmi.org

ABSTRACT

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d.

Contact: pengh@janelia.hhmi.org.

Show MeSH