Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

Abstract

Background: Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, singleneuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files. Results: We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise. Conclusions: This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

NRRS: a re-tracing strategy to refine neuron reconstruction

“…One drawback for MS is that a maximum searching distance parameter needs to be provided for constraining the solution space, which is hardly general for a complete neuronal reconstruction crossing thousands of imaging blocks. Other potential solutions to the limitations of Mean-Shift (MS) algorithm for refining neurite reconstruction include 3D shape restriction Mean-Shift ( Jiang et al , 2020 ) and an optimization algorithm based on the Lasso approach ( Li et al , 2019 ). 3D shape restriction Mean-Shift can lead each processing reconstruction unit, typically a node in SWC file, to a locally optimal position without considering the rationality of the entire fitting results.…”

SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline

“…In the literature, there are several papers describing post-processing of automated reconstruction results using various methods, e.g., G-Cut (Li R. et al, 2019 ), ray-shooting based repairer (Yu et al, 2021 ), and solemnization algorithm (Jiang et al, 2020 ). However, only some of these studies focused on the pruning of results.…”

A robust approach to 3D neuron shape representation for quantification and classification

“…First, we need to have a good shape representation of each neuron. Skeleton representations are widely used in neuroscience [ 2 – 5 ] as they provide a compact and abstract shape representation. Mathematically, skeletonization or medial axis transform (MAT) has a rigorous definition for arbitrary shapes.…”