 Research article
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 Published:
A distancefield based automatic neuron tracing method
BMC Bioinformatics volume 14, Article number: 93 (2013)
Abstract
Background
Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.
Results
We have developed a novel approach, named DFTracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DFTracing executes a coupled distancefield (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distancetransform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can “push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.
Conclusion
We have used DFTracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DFTracing produces better reconstructions.
Background
In neuroscience it is important to accurately trace, or reconstruct, a neuron’s 3D morphology. The current neuron tracing methods can be described, according to the necessary manual input, as being manual, semiautomatic or fully automatic. Neurolucida (MBF Bioscience), a largely manual technique, uses straight linesegments to connect manually determined neuron skeleton locations drawn from the 2D crosssectional views of a 3D image stack. In contrast, semiautomatic methods need some prior information, such as the termini of a neuron, for the automated process to find the neuron skeleton. For example, the semiautomatic Vaa3DNeuron 1.0 system (previously called “V3DNeuron”) [1, 2] has been used in systematical and largescale reconstructions of single neurons/neuritetracts from mouse and fruitfly [3, 4].
However, for very complicated neuron structures and/or massive amounts of image data, the semiautomatic methods are still timeconsuming. Thus, a fully automated tracing method is currently highly desired. Early fully automated methods used image thinning to extract skeletons from binary images [57].
These methods iteratively remove voxels from the segmented foregroun region surface of an image. In addition, neurontracing approaches based on pattern recognition were also developed ([813]). However, in cases of low image quality, the tracing accuracy may be greatly compromised. The modelbased approaches, such as those that use a 3D line, sphere or cylinder for identifying and tracing the morphological structures of neurons, are relatively more successful ([1417]). These methods can also be guided using both global prior information and local salient image features ([2, 18, 19]). While the basis of most existing methods is to grow a neuron structure from a predefined or automatically selected “seed” location, the allpath pruning method [20] that iteratively removes the redundant structural elements was recently proposed as a powerful alternative.
Despite such a large number of proposed neuron tracing algorithms ([14, 21]), few can automatically trace complicated neuron structures set in noisecontaminated microscopic images (Figure 1 (a) and (b)). Here we report a new method, named DFTracing (‘DF’ for “Distance Field”), which meets this challenge. We tested DFTracing with very elaborate images of dragonfly neurons. Without any human intervention, DFTracing produced a good reconstruction (Figure 1 (c) and (d)), comparable in quality to that of human manual work.
Method
A reconstructed neuron (e.g. Figure 1 (c) and (d)) has a treelike structure and can be viewed as the aggregation of one or more neurite segments. Each segment is a curvilinear structure similar to Figure 2. When a neuron has multiple segments, they are joined at branching points. The neuron structure can thus be described with a SWC format [22], where there are a number of reconstruction nodes and edges. Each node stands for a 3D spatial location (x,y,z) on the neuron’s skeleton. Each edge links a node to its parent (when a node has no parent, then its parent is flagged as 1). The crosssectional diameter of the neuron at the location of each node is also calculated and included in SWC format. Therefore, to produce a neuron reconstruction, two key components are (a) determination of the skeleton, i.e. ordered sequence of reconstruction nodes, of this neuron, and (b) estimation of the diameters at each node’s location.
An intuitive way to trace a neuron is to start from a predefined or automatically computed location, called “seed”, to grow the neuron morphology until it covers all visible signals in the image. If the image foreground region that corresponds to a neuron can be well extracted, the problem reduces to determine the complete set of reconstruction nodes, i.e. skeletonize the neuron, using the foreground mask of the image, followed by estimating the crosssectional diameter of all reconstruction nodes.
We follow this intuition and design the following threestage neurontracing algorithm:
(Step 1) Enhance linelike structures in the image, followed by adaptive thresholding to remove nonneuron background and noise. (2.1)
(Step 2) Skeletonize the neuron using coupled distance fields. (2.2)
(Step 3) Assemble multiple spatially disconnected pieces of the traced neuron into the final result. (2.3)
Preprocessing: extraction of neuron signal
Since a neuron segment looks like a line or a tube (Figure 2), anisotropic filtering of image noise can enhance the neuron signal in an image. We use nonlinear anisotropic filtering for signal enhancement, followed by automated thresholding.
The key idea of anisotropic filtering is to calculate image features that signify the orientation preference of local image areas. We follow the classic Hessian matrix based method, which detects the curvilinear structures in images [23]. Let u(x,y,z) stand for an image, where x, y, z are the spatial coordinates. We use ∇u to denote the image intensity gradient. A filtered image pixel will take the following value v,
The function f(u) is defined using the Hessian matrix of each image pixel u,$H=\left[\begin{array}{ccc}\hfill {u}_{\mathit{xx}}\hfill & \hfill {u}_{\mathit{xy}}\hfill & \hfill {u}_{\mathit{xz}}\hfill \\ \hfill {u}_{\mathit{yx}}\hfill & \hfill {u}_{\mathit{yy}}\hfill & \hfill {u}_{\mathit{yz}}\hfill \\ \hfill {u}_{\mathit{zx}}\hfill & \hfill {u}_{\mathit{zy}}\hfill & \hfill {u}_{\mathit{zz}}\hfill \end{array}\right],$ where H is indeed symmetric. Of note, the Hessian method has been well used in medical image computing, especially vessel enhancement and segmentation ([24, 25]).
To do so, we compute the eigenvalues of H, denoted as λ_{1}, λ_{2}, and λ_{3} (λ_{1} ≥ λ_{2} ≥ λ_{3}). Interestingly, when the brighter pixel intensity indicates the stronger signal and a linelook structure is present at the current pixel location, there is λ_{1} ≈ 0 and λ_{1} >> {λ_{2}, λ_{3}} [23]. Therefore, we explicitly detect if such conditions will be met for each pixel, and define the following function f(u).
where α_{ i } (i = 1, 2, 3) are predefined coefficients (α_{1}=0.5, α_{2}=0.5, α_{3}=25), ${k}_{i}=\text{exp}\left({\lambda}_{i}^{2}/{\sum}_{i}^{3}{=}_{1}{\lambda}_{i}^{2}\right)$.
After signal enhancement, extracting the neurite foreground using global thresholding is straightforward. We determine the threshold with the following iterative process. First, the average image intensity is taken as a candidate threshold. We use this candidate threshold to divide the image into two portions: pixels with higher, or lower intensity. We then calculate the average of the two mean intensity values of these two portions and use it as a new candidate threshold. This process is iterated until the candidate threshold value no longer changes. This converging threshold is used as the final global threshold: any image pixel with intensity higher than this threshold is part of the so called “image foreground”, which is assumed to contain the neuron signal.
Lastly, since the 3Dextracted image foreground could contain multiple neurite areas and noise, neuron reconstruction is carried out in individual areas, which are “stitched” together via postprocessing. Discarding the very small pieces (smaller than 10 pixels) removes the noise still present in the image foreground.
Neuron tracing using coupled distance fields
Figure 2 shows that the skeleton of a neuron segment is essentially the medial axis of this segment. How can the medial axis be recovered? Possibly the simplest way is image thinning, which unfortunately has two wellknown problems: (a) sensitivity to orientation of the image region of interest, and (b) unnecessary forks of the skeleton at the ends of the image region. Another intuitive approach is to use tube fitting (e.g. [17]) or a rolling ball fitting. Below, we present a simple method that works robustly without a parameter.
Distance transform of an image region R with respect to another image region T is defined as for each image pixel in R, replace its intensity using the shortest distance to T. Typically T is selected as the image background, but it can also be selected as any specific image pixel. We have the following observation of Figure 2.

In the distance transform of a neuron segment with respect to an arbitrarily selected seed location, s, at a tip point of the neuron segment, the distancetransformed pixel intensity has a larger value than those of the nearby foreground pixels. In another word, any tip/terminal point in a neuron structure will have a local maximum on the boundary pixels in this distance field.

In the distance transform of a neuron segment to the image background, the boundary pixels will have value 1 and the skeleton points will form a ridge of local maximal values, compared to all other image pixel locations that are orthogonal to the tangent direction of the skeleton. Extraction of the ridge will skeletonize the neuron segment.

Assume we have a rolling ball that is pushed forward by these two coupled distance “force” fields. It can be seen that this ball will move toward the skeleton from any starting location and then along the ridge curve. Following this trajectory we can extract the neuron skeleton quickly. For this reason, in Figure 2 we call these two force fields “thrust” (for F_{1}, which is the distance transform field of the foreground pixels to an automatically determined seed point) and “pressure” (for F_{2}, which is the distance transform field of foreground pixels (voxels) to the background) fields, respectively.

Multiple traced neuron segments can be merged at their convergence point to reconstruct the treelike structure of a neuron.
Based on these observations, we have designed the following DFTracing algorithm:

(1)
Detect neuron region(s) in 3D using method in 2.1. Multiple spatially nonconnected neuron regions may be produced.

(2)
For each neuron region R, find the set of boundary pixels B, which is defined the set of pixels having at least one neighboring pixel (26neighbors in 3D) as the background.

(3)
Arbitrarily select a seed location, s, from the boundary pixel set B.

(4)
Compute both the thrust and pressure distance fields with respect to s and B, using the entire image.

(5)
In the thrust field, detect the set of all local maxima locations, M.

(6)
For each point t ∈ M, set an initially empty skeleton C(t) to {t}. Denote the last added skeleton point as p (initially p = t).

(7)
Denote π(p) = R ∩ Ω(p) (Ω is the 26neighborhood of a pixel in 3D). For all pixels q ∈ π(p), find q* that has the largest pressure field value.

(8)
If q* also has a lower thrust field value (note that now we are backtracing a path) than p, then add q* to the skeleton set.

(9)
Assign q* to p, and repeat steps 6 and 7 until the seed location s is met. This completes the skeleton of a neuron segment C(t).

(10)
Merge the common portion of multiple skeletons.

(11)
Use the pressure field value of skeleton points as the respective values for the radius estimation.

(12)
Assemble multiple neurite reconstructions for all neuron regions using the postprocessing method in 2.3.
In its implementation, our DFTracing algorithm can be further optimized. Indeed, steps 69 can be parallelized. Instead of finding the complete skeleton for each terminal point t ∈ M sequentially, we can grow every skeleton one step at a time in parallel. A skeleton stops growing when either the seed location or any other skeleton pixel location has been reached. This process iterates until all skeletons stop growing. The parallelized algorithm will also avoid step 9, i.e. merging common portions of skeletons. In addition, to save computational time when calculating the distance fields, we use the city block distance [26] instead of the Euclidean distance.
Postprocessing: produce the complete reconstruction
The neurontracing algorithm in 2.2 can return a treelike structure for a single 3D connected neuron region. In case that the neuron foreground extraction (2.1) produces multiple spatial disconnected neuron regions, we will have multiple neuron trees. Often these pieces need to be assembled into a full reconstruction. Since a gap between two disconnected neuron regions is typically small; only the nearby pieces with a separation smaller than two times the radius of the nearest nodes are connected. Then, an arbitrarily selected “root” location (usually the first leaf node in the SWC representation) is used to sort the order (i.e. parentchildren relationship) of all neuron reconstruction nodes. Finally, pruning the very short branches whose lengths are less than two pixels completes the reconstruction.
Pros and cons of DFtracing, and comparison to other methods
DFTracing is an efficient, deterministic, and essentially parameterfree method (for the core part of coupled distance fields). Compared to many previous neurontracing methods, this new method avoids the complication introduced by the previous need to select parameters. In addition, DFTracing is a local search method, similar to the major body of existing neurontracing techniques. While local search cannot guarantee the global correctness of the final reconstruction, compared to those use the global guiding information (e.g. Vaa3DNeuron 1.0), DFTracing has the advantage that it uses a smaller amount of computer memory. Moreover, we note that the termini produced in the thrust field (step 5 in the DFTracing algorithm) could be used as global guiding prior information for the Vaa3DNeuron system. In that sense, the steps 610 in DFTracing is basically equivalent to the shortest path algorithm in the graphaugmented deformable model [2]. However, DFTracing does not need to literally produce the graph of image pixels and thus uses less computer memory.
DFTracing has several caveats that deserve further improvement. First, it is based on distance transform, which may be sensitive to neuron boundary and also anisotropic neuron structure in 3D images. This can be refined in the future by (1) replacing distance transform with a more robust statistical test method, similar to the diameter estimation method used in Vaa3DNeuron and (2) smoothing the contour/edge of the extracted image foreground to make the distance transform more robust to image noise. Second, the postprocessing of DFTracing can be further improved by adding machine learning methods (e.g. [27]). Third, the preprocessing step can be further improved using a multiscale anisotropic filtering approach [23].
Results and discussion
To assess the DFTracing method, we consider several datasets, especially one consisting of 3D confocal images of dragonfly neurons (species L. luctuosa), which have very complicated neuron arborization patterns, heavy noise and uneven image background (Figure 1 (a) and (b)). We also tested DFTracing using neuron images from other organisms, such as the fruit fly (species D. melanogaster). We compared DFTracing with existing automatic approaches, especially NeuronStudio [28] and iTube [17], and the semiautomatic approach Vaa3DNeuron 1.0 ([1]).
We tested the neurontracing algorithm on 3D confocal image stacks of neurons in dragonfly (data set was obtained from [29]) and Drosophila (data from the Digital Reconstruction of Axonal and Dentritic Morphology (DIADEM) competition (http://www.diademchallenge.org).
Neuron signal enhancement and neuron tracing
Our dragonfly image stacks are noisy and have low contrast (Figure 1(a) and (b), thus they are good test cases to examine the neuron signal enhancement method in 2.1. We added Poisson noise to the original image and compared the results to Gaussianfilter based denoising. As shown in Figure 3, our anisotropic filtering method is able to produce much better peak signaltonoise ratio (PSNR) ([30]). Visually, our method also preserves and enhances the neurite signal significantly.
We then used DFTracing to trace the neuron in Figure 3 (a). After filtering, there are many disconnected neuron regions (Figure 3 (b)). DFTracing successfully traced all individual regions and merged the final result (Figure 4). The final tracing result faithfully replicates the original neuron morphology. A few small branches are missing (Figure 4 (b)), which are due to the low image quality in the respective image areas. In summary, Figure 4 demonstrates that both the signal enhancement and tracing modules in the DFTracing algorithm yield a high performance.
Neuron tracing: comparison with other methods
We compared DFTracing to the following three neurontracing programs, which are publicly available and have been used to produce several recent significant results in neuroscience.

(1)
Vaa3DNeuron 1.0 semiautomatic tracing ([2]);

(2)
NeuronStudio automatic tracing (0.9.92 version, http://research.mssm.edu/cnic/toolsns.html);

(3)
iTube automatic tracing [17];
To compare the key tracing modules of different methods, we used one confocal image of fruitfly olfactory projection neuron, which has a very high signaltonoise ratio and is also used in several previous studies ([31]). We binarized this image for testing.
We also compared the tracing performance of these methods for complicated neuron morphology using the dragonfly neurons. Due to the complexity of the neuron structure, it is very difficult to manually determine the end points of neurons within a day, thus it is impractical to directly use Vaa3DNeuron 1.0 for these dragonfly neurons. Figures 5 and 6 show the tracing results produced by both NeuronStudio and iTube. Both methods missed a number of branches, whereas DFTracing reasonably recaptured the neurons’ morphology.
Neuron tracing: quantitative analysis
Figure 7 compares the results between tracing methods. With Vaa3DNeuron, we manually selected all endpoints of this neuron and inspected the results to produce the “ground truth” for evaluation (Figure 7 (a)). We selected a total of 17 points. It is apparent that the NeuronStudio result misses many branches (Figure 7 (b)). The iTube result includes most branches, but still misses a few, especially the highly curved structures (Figure 7 (c)). DFTracing produced the same result as the ground truth version (Figure 7 (d)). Table 1 summarizes the comparison quantitatively. It is clear that the DFTracing result is the best among these methods as all branches were correctly traced.
We also compared the tracing results for a nonbinary confocal image. We chose a local region of the dragonfly neuron (Figure 8 (a)). We also selected all endpoint (totally 27) of this neuron and inspected the results to “ground truth” for evaluation (Figure 8 (b)). The NeuronStudio and iTube results miss many branches (Figure 8 (b) and (Figure 8 (c)). DFTracing produced the same result as the ground truth version (Figure 8 (d)). Table 2 summarizes the comparison quantitatively. There was also clear that the DFTracing result produced the best performance among these methods, as most branches were correctly traced.
With regards to running time for tracing, Vaa3DNeuron 1.0 is still faster than the new DF methods, although it requires some humaninteraction time, with an execution time of around 10 seconds for the Figure 7 on an Intel Q6600 processor (2.40 GHz). The tracing time for Figure 8 is around 22 seconds. Table 3 summarizes the comparison time of different methods. It is clear that the time of DFTracing is currently the slowest of all automatic methods, but its accuracy is the best. However, it should be noted that the operations of DFTracing can be parallelized, and thus in a future implementation we hope to accelerate the speed by orders of magnitude through the use of multicore processors and graphics processing units (GPUs).
Neuron tracing: robustness
We tested the robustness of DFTracing. We added Gaussian white noise of mean 0 and different variance v to the image in Figure 7(a), where v = 0.01, 0.02, 0.03 and 0.05 respectively. In this way, multiple reconstruction results were produced As shown in Figure 9. We computed the pairwise spatial distance (SD) score of these reconstructions, as defined in [1]. The average SDs is 0.149 pixel, very close to 0.
We also tested the robustness of DFTracing for a nonbinary confocal image. We added Gaussian white noise of mean 0 and different variance v to the image in Figure 8(a), where v = 0.01, 0.02, 0.03 and 0.05 respectively. In this way, multiple reconstruction results were produced. As shown in Figure 10, the test image contains various levels of noise. For example, when v = 0.05, most signals of the image have been contaminated, yet we can trace major neuron branching in the remaining visible image regions. We computed the pairwise SD score of these reconstructions and the average of SDs is 0.62 pixel. These experiments demonstrate that our method can produce consistent and robust reconstructions.
Automatic tracing of complicated morphology of many neurons
In Figure 11, we tested the performance of DFTracing on 20 dragonfly neurons (thoracic ganglia) that have various levels of complexity and background noises. DFTracing reconstructed the morphology within a day on a MacBook Pro laptop. Fully manual tracing of same set of data would need at least tens of days. The biologist (PGB) in this study visually inspected all results and found all major neuron trunks and branches have been correctly traced. Of note, the complexity of morphology and high noise level make it very hard to produce faithful manual tracing, thus DFTracing is evidently a meaningful solution to this data set.
Conclusion
We have developed a automatic neuron tracing method, DFTracing, that outperformed several previous automatic and semiautomatic methods in a very challenging set of dragonfly neurons with complex morphology and high noise levels. This method is efficient and essentially parameterfree. DFTracing has application potential in largescale neuron reconstruction and anatomy projects.
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Acknowledgements
In DFTracing we use a neuronsorting module developed by Yinan Wan and Hanchuan Peng for poststitching multiple neuron segments. We thank Anthony Leonardo for providing the equipment necessary for the neuron labeling and image data collection, Ting Zhao for assistance in comparing the iTube software. We thank Erhan Bas and Margaret Jefferies for proofreading this manuscript and suggestions. This research is supported by Howard Hughes Medical Institute. This work was also supported by Chinese National Natural Science Foundation under grant No.61001047 and 61172002.
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Authors’ contributions
HP conceived and supervised the study; JY designed algorithms; JY and HP wrote program and performed data analysis; PGB assisted in data analysis; all authors participated in the preparation of this manuscript. All authors read and approved the final manuscript.
Jinzhu Yang and Hanchuan Peng contributed equally to this work.
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Yang, J., GonzalezBellido, P.T. & Peng, H. A distancefield based automatic neuron tracing method. BMC Bioinformatics 14, 93 (2013). https://doi.org/10.1186/147121051493
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Keywords
 Neuron Structure
 Foreground Pixel
 Neuron Region
 Distance Field
 Boundary Pixel