Anisotropic path searching for automatic neuron reconstruction

UMMS Affiliation

Department of Neurobiology



Document Type


Medical Subject Headings

*Algorithms; Animals; Anisotropy; Drosophila; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Microscopy; Neurons; Pattern Recognition, Automated


Neuroscience and Neurobiology


Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila's projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

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Citation: Med Image Anal. 2011 Oct;15(5):680-9. Epub 2011 May 27. Link to article on publisher's site

Related Resources

Link to Article in PubMed