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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.

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Reconstructions produced for the same original neuron image, but contaminated by different levels of noise. (a) A part of the original image, where there are some dark regions that are hard for tracing. (b) A noise image where bright voxels are randomly deleted. As a result, the bouton region becomes fragmented. The noise introduced is a random deletion of q% of bright voxels (intensity >50). In this sub-figure, q=75. (c) The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Red: the reconstruction from noise-free image. Green, orange, yellow and magenta: q is 25, 50, 75 and 90, respectively. When q=90, most signals of the image have been removed. See summary result in Table 1 as well.
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Figure 7: Reconstructions produced for the same original neuron image, but contaminated by different levels of noise. (a) A part of the original image, where there are some dark regions that are hard for tracing. (b) A noise image where bright voxels are randomly deleted. As a result, the bouton region becomes fragmented. The noise introduced is a random deletion of q% of bright voxels (intensity >50). In this sub-figure, q=75. (c) The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Red: the reconstruction from noise-free image. Green, orange, yellow and magenta: q is 25, 50, 75 and 90, respectively. When q=90, most signals of the image have been removed. See summary result in Table 1 as well.

Mentions: Figure 7 shows that with as much as 75% of visible voxels have been randomly removed from this image (Fig. 7b), APP is still able to produce an overall meaningful reconstruction (Fig. 7c), which has a similar skeleton with the reconstruction obtained from the noise-free image. Most distinct areas of these reconstructions happen at the bouton regions, where most bright voxels are deleted and thus this area has a number of fragments. Interestingly, most of the skeleton regions remain very similar. This demonstrates the robustness of APP.Fig. 7.


Automatic 3D neuron tracing using all-path pruning.

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

Reconstructions produced for the same original neuron image, but contaminated by different levels of noise. (a) A part of the original image, where there are some dark regions that are hard for tracing. (b) A noise image where bright voxels are randomly deleted. As a result, the bouton region becomes fragmented. The noise introduced is a random deletion of q% of bright voxels (intensity >50). In this sub-figure, q=75. (c) The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Red: the reconstruction from noise-free image. Green, orange, yellow and magenta: q is 25, 50, 75 and 90, respectively. When q=90, most signals of the image have been removed. See summary result in Table 1 as well.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 7: Reconstructions produced for the same original neuron image, but contaminated by different levels of noise. (a) A part of the original image, where there are some dark regions that are hard for tracing. (b) A noise image where bright voxels are randomly deleted. As a result, the bouton region becomes fragmented. The noise introduced is a random deletion of q% of bright voxels (intensity >50). In this sub-figure, q=75. (c) The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Red: the reconstruction from noise-free image. Green, orange, yellow and magenta: q is 25, 50, 75 and 90, respectively. When q=90, most signals of the image have been removed. See summary result in Table 1 as well.
Mentions: Figure 7 shows that with as much as 75% of visible voxels have been randomly removed from this image (Fig. 7b), APP is still able to produce an overall meaningful reconstruction (Fig. 7c), which has a similar skeleton with the reconstruction obtained from the noise-free image. Most distinct areas of these reconstructions happen at the bouton regions, where most bright voxels are deleted and thus this area has a number of fragments. Interestingly, most of the skeleton regions remain very similar. This demonstrates the robustness of APP.Fig. 7.

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