Anisotropic path searching for automatic neuron reconstruction

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.

A template-free approach is introduced for automating the 3D reconstruction of neuritis from microscopy volumes. The complex neuronal structures are captured with an efficient adaptive seeding method and the tracing problem is solved by computing the optimal reconstruction from the weighted graph constructed with those optimal seeds. The proposed algorithm is computational efficient and has generated promising results on various sets of microscopy images.■■Figure optionsDownload high-quality image (103 K)Download as PowerPoint slideHighlights
► A efficient algorithm for automatic neuron reconstruction.
► The algorithm can handle complex structures adaptively and optimize the localization of bifurcations.
► A reliable technique to compare various of neurons for tracing evaluation and neuron retrival.