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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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Related in: MedlinePlus

Neuron tracing/reconstruction from images and SC examples of a reconstruction. (a) A 3D reconstruction of a fruit fly neuron. (b) The entire morphology model can typically be decomposed as individual segments (shown in different colors), which are connected at the branching points. Typically, each segment can be traced/reconstructed separately. (c) The zoom-in view of the BOX1 in (a) and (b). (d) Illustration of the modeling of image voxel information using a series of spherical SCs. The edge of image region (bright voxels) best matches to the aggregation of SCs. (e) Other types of SCs besides spheres, such as ellipsoids and cylinders, can be used in locally matching the image content and thus growing the reconstruction. (f) A maximum intensity project of a 3D stack of a fruit fly lamina neuron (courtesy of G. Rubin lab, Janelia Farm, HHMI). The neurites are highly punctuated, have high contrast in image intensity and appear to be broken. (g) Stained CA3 pyramidal neuron of a mouse brain region (courtesy of R. Tsien lab, Stanford University), where axonal varicosities make it hard to grow a reconstruction using local searching based on SCs.
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Figure 1: Neuron tracing/reconstruction from images and SC examples of a reconstruction. (a) A 3D reconstruction of a fruit fly neuron. (b) The entire morphology model can typically be decomposed as individual segments (shown in different colors), which are connected at the branching points. Typically, each segment can be traced/reconstructed separately. (c) The zoom-in view of the BOX1 in (a) and (b). (d) Illustration of the modeling of image voxel information using a series of spherical SCs. The edge of image region (bright voxels) best matches to the aggregation of SCs. (e) Other types of SCs besides spheres, such as ellipsoids and cylinders, can be used in locally matching the image content and thus growing the reconstruction. (f) A maximum intensity project of a 3D stack of a fruit fly lamina neuron (courtesy of G. Rubin lab, Janelia Farm, HHMI). The neurites are highly punctuated, have high contrast in image intensity and appear to be broken. (g) Stained CA3 pyramidal neuron of a mouse brain region (courtesy of R. Tsien lab, Stanford University), where axonal varicosities make it hard to grow a reconstruction using local searching based on SCs.

Mentions: Digital reconstruction, or tracing, of 3D neuron structures (Fig. 1) is critical toward reverse engineering the wiring and functions of a brain (Roysam et al., 2009; Peng et al., 2011b). A number of studies (e.g. Al-Kofahi et al., 2002, 2003; Abdul-Karim et al., 2005; Cai et al., 2008; Dima et al., 2002; Evers et al., 2005; Losavio et al., 2008; Meijering et al., 2004; Narro et al., 2007; Peng et al., 2010a, 2010b, 2011a; Rodriguez et al., 2009; Schmitt et al., 2004; Sun et al., 2009; Vasilkoski et al., 2009; Wearne et al., 2005; Weaver et al., 2004; Xie et al., 2010; Xiong et al., 2006; Yuan et al., 2009; Zhang et al., 2007, 2008, 2011) have been conducted to develop semi- or fully automatic neuron-tracing methods that would yield more efficient neuron reconstruction than the currently widely adopted manual reconstruction strategy. Most of these existing methods have used various structural components (SC), e.g. 3D spheres, ellipsoids, cylinders, lines segments or irregular compartments, to model a neuron's morphology (Fig. 1a–e). The most successful strategy among these algorithms is to build-up the reconstruction by incrementally adding more and more such SCs into the morphological modeling of a neuron. Good examples include image voxel scooping (Rodriguez et al., 2009), ray shooting (Wearne et al., 2005) and template matching (Zhao et al., 2011). These bottom-up local searching methods are suitable for 3D images that have ideally continuous neurite tracts and good signal-to-noise ratio (SNR).Fig. 1.


Automatic 3D neuron tracing using all-path pruning.

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

Neuron tracing/reconstruction from images and SC examples of a reconstruction. (a) A 3D reconstruction of a fruit fly neuron. (b) The entire morphology model can typically be decomposed as individual segments (shown in different colors), which are connected at the branching points. Typically, each segment can be traced/reconstructed separately. (c) The zoom-in view of the BOX1 in (a) and (b). (d) Illustration of the modeling of image voxel information using a series of spherical SCs. The edge of image region (bright voxels) best matches to the aggregation of SCs. (e) Other types of SCs besides spheres, such as ellipsoids and cylinders, can be used in locally matching the image content and thus growing the reconstruction. (f) A maximum intensity project of a 3D stack of a fruit fly lamina neuron (courtesy of G. Rubin lab, Janelia Farm, HHMI). The neurites are highly punctuated, have high contrast in image intensity and appear to be broken. (g) Stained CA3 pyramidal neuron of a mouse brain region (courtesy of R. Tsien lab, Stanford University), where axonal varicosities make it hard to grow a reconstruction using local searching based on SCs.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 1: Neuron tracing/reconstruction from images and SC examples of a reconstruction. (a) A 3D reconstruction of a fruit fly neuron. (b) The entire morphology model can typically be decomposed as individual segments (shown in different colors), which are connected at the branching points. Typically, each segment can be traced/reconstructed separately. (c) The zoom-in view of the BOX1 in (a) and (b). (d) Illustration of the modeling of image voxel information using a series of spherical SCs. The edge of image region (bright voxels) best matches to the aggregation of SCs. (e) Other types of SCs besides spheres, such as ellipsoids and cylinders, can be used in locally matching the image content and thus growing the reconstruction. (f) A maximum intensity project of a 3D stack of a fruit fly lamina neuron (courtesy of G. Rubin lab, Janelia Farm, HHMI). The neurites are highly punctuated, have high contrast in image intensity and appear to be broken. (g) Stained CA3 pyramidal neuron of a mouse brain region (courtesy of R. Tsien lab, Stanford University), where axonal varicosities make it hard to grow a reconstruction using local searching based on SCs.
Mentions: Digital reconstruction, or tracing, of 3D neuron structures (Fig. 1) is critical toward reverse engineering the wiring and functions of a brain (Roysam et al., 2009; Peng et al., 2011b). A number of studies (e.g. Al-Kofahi et al., 2002, 2003; Abdul-Karim et al., 2005; Cai et al., 2008; Dima et al., 2002; Evers et al., 2005; Losavio et al., 2008; Meijering et al., 2004; Narro et al., 2007; Peng et al., 2010a, 2010b, 2011a; Rodriguez et al., 2009; Schmitt et al., 2004; Sun et al., 2009; Vasilkoski et al., 2009; Wearne et al., 2005; Weaver et al., 2004; Xie et al., 2010; Xiong et al., 2006; Yuan et al., 2009; Zhang et al., 2007, 2008, 2011) have been conducted to develop semi- or fully automatic neuron-tracing methods that would yield more efficient neuron reconstruction than the currently widely adopted manual reconstruction strategy. Most of these existing methods have used various structural components (SC), e.g. 3D spheres, ellipsoids, cylinders, lines segments or irregular compartments, to model a neuron's morphology (Fig. 1a–e). The most successful strategy among these algorithms is to build-up the reconstruction by incrementally adding more and more such SCs into the morphological modeling of a neuron. Good examples include image voxel scooping (Rodriguez et al., 2009), ray shooting (Wearne et al., 2005) and template matching (Zhao et al., 2011). These bottom-up local searching methods are suitable for 3D images that have ideally continuous neurite tracts and good signal-to-noise ratio (SNR).Fig. 1.

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
Related in: MedlinePlus