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.
[1]
G. Ascoli.
Computational Neuroanatomy
,
2002,
Humana Press.
[2]
Jan G. Bjaalie,et al.
Understanding the Brain through Neuroinformatics
,
2008,
Front. Neurosci..
[3]
James A. Sethian,et al.
Level Set Methods and Fast Marching Methods
,
1999
.
[4]
D. Chklovskii,et al.
Geometry and Structural Plasticity of Synaptic Connectivity
,
2002,
Neuron.
[5]
H. Vincent Poor,et al.
An Introduction to Signal Detection and Estimation
,
1994,
Springer Texts in Electrical Engineering.
[6]
Kaleem Siddiqi,et al.
Flux maximizing geometric flows
,
2001,
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[7]
Erik Meijering,et al.
Neuron tracing in perspective
,
2010,
Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[8]
Giorgio A. Ascoli,et al.
The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron
,
2011,
Neuroinformatics.
[9]
Junaed Sattar.
Snakes , Shapes and Gradient Vector Flow
,
2022
.
[10]
Laurent D. Cohen,et al.
Global Minimum for Active Contour Models: A Minimal Path Approach
,
1997,
International Journal of Computer Vision.
[11]
D. Chklovskii,et al.
Neurogeometry and potential synaptic connectivity
,
2005,
Trends in Neurosciences.