Paper
24 February 2012 Automated reconstruction of neural trees using front re-initialization
Amit Mukherjee, Armen Stepanyants
Author Affiliations +
Abstract
This paper proposes a greedy algorithm for automated reconstruction of neural arbors from light microscopy stacks of images. The algorithm is based on the minimum cost path method. While the minimum cost path, obtained using the Fast Marching Method, results in a trace with the least cumulative cost between the start and the end points, it is not sufficient for the reconstruction of neural trees. This is because sections of the minimum cost path can erroneously travel through the image background with undetectable detriment to the cumulative cost. To circumvent this problem we propose an algorithm that grows a neural tree from a specified root by iteratively re-initializing the Fast Marching fronts. The speed image used in the Fast Marching Method is generated by computing the average outward flux of the gradient vector flow field. Each iteration of the algorithm produces a candidate extension by allowing the front to travel a specified distance and then tracking from the farthest point of the front back to the tree. Robust likelihood ratio test is used to evaluate the quality of the candidate extension by comparing voxel intensities along the extension to those in the foreground and the background. The qualified extensions are appended to the current tree, the front is re-initialized, and Fast Marching is continued until the stopping criterion is met. To evaluate the performance of the algorithm we reconstructed 6 stacks of two-photon microscopy images and compared the results to the ground truth reconstructions by using the DIADEM metric. The average comparison score was 0.82 out of 1.0, which is on par with the performance achieved by expert manual tracers.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amit Mukherjee and Armen Stepanyants "Automated reconstruction of neural trees using front re-initialization", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141I (24 February 2012); https://doi.org/10.1117/12.912237
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Reconstruction algorithms

Neurons

Image processing

Diffusion

Image segmentation

Microscopy

3D image reconstruction

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