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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
Consistent reconstructions produced for the same neuron from different seed locations. The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Dots: reconstruction nodes. Root/seed nodes are bigger dots. The major difference of these reconstructions happens around the locations of their seed locations.
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Figure 6: Consistent reconstructions produced for the same neuron from different seed locations. The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Dots: reconstruction nodes. Root/seed nodes are bigger dots. The major difference of these reconstructions happens around the locations of their seed locations.

Mentions: In our experiment, we traced it 20 times from 20 randomly selected, and spatially distant seeds. The reconstructions are very similar (Fig. 6). The number of reconstruction nodes range from 390 to 403, with mean±standard deviation=398 ± 3.34 voxels. The average SD score between R1 and the 19 other reconstructions is 0.215 voxels over the entire structure. The average SSD% score is only 2.79%. For these visible distinct regions, the average SSD score is only 3.0 voxels. Therefore, the overall structures traced from APP are consistent.Fig. 6.


Automatic 3D neuron tracing using all-path pruning.

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

Consistent reconstructions produced for the same neuron from different seed locations. The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Dots: reconstruction nodes. Root/seed nodes are bigger dots. The major difference of these reconstructions happens around the locations of their seed locations.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 6: Consistent reconstructions produced for the same neuron from different seed locations. The final skeletons of reconstructions are intentionally displaced for better visualization. Different colors indicate different reconstructions. Dots: reconstruction nodes. Root/seed nodes are bigger dots. The major difference of these reconstructions happens around the locations of their seed locations.
Mentions: In our experiment, we traced it 20 times from 20 randomly selected, and spatially distant seeds. The reconstructions are very similar (Fig. 6). The number of reconstruction nodes range from 390 to 403, with mean±standard deviation=398 ± 3.34 voxels. The average SD score between R1 and the 19 other reconstructions is 0.215 voxels over the entire structure. The average SSD% score is only 2.79%. For these visible distinct regions, the average SSD score is only 3.0 voxels. Therefore, the overall structures traced from APP are consistent.Fig. 6.

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