Abstract
Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.
Highlights
Neuron reconstruction is an important technique in many areas of brain research to identify neuron types, examine neuronal connections, or investigate neuronal circuits
We evaluated the performance of our model in detecting the skeleton of tortuous structures
The first issue is that a number of methods adapt a simple way to detect branch points around complex branch structures
Summary
Neuron reconstruction is an important technique in many areas of brain research to identify neuron types, examine neuronal connections, or investigate neuronal circuits. It has been a focus of neuronal image analysis for years (Meijering, 2010; Lu, 2011; Peng et al, 2015). The radii and signal intensity vary a lot for neurites near branch points, which form a complex morphology. These two features are commonly found in neurons but cannot be captured by a parameter model
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