Yang Jian, He Yishan, Liu Xuefeng
Faculty of Information Technology, Beijing University of Technology, Beijing, China.
Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing, China.
Brain Inform. 2020 Nov 4;7(1):14. doi: 10.1186/s40708-020-00117-x.
Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar substructures on one or more neuron reconstructions, which are very similar to a marked problematic substructure. The pipeline consists of four steps: getting a marked substructure, constructing a query substructure, generating candidate substructures and retrieving most similar substructures. The retrieval procedure was tested on 163 gold standard reconstructions provided by the BigNeuron project and a reconstruction of a mouse's large neuron. Experimental results showed that the implementation of the proposed methods is very efficient and all retrieved substructures are very similar to the marked one in numbers of nodes and branches, and degree of curvature.
由于手动追踪耗时且自动追踪性能不稳定，高效且有效地生成准确的神经元重建仍然是一项具有挑战性的任务。一种策略是自动生成重建，然后手动修正其不准确的部分。为了有效地找到不准确的子结构，我们提出了一个流程，用于在一个或多个神经元重建上检索与标记的有问题子结构非常相似的子结构。该流程包括四个步骤：获取标记的子结构、构建查询子结构、生成候选子结构以及检索最相似的子结构。该检索过程在由BigNeuron项目提供的163个金标准重建以及一只小鼠的大型神经元重建上进行了测试。实验结果表明，所提出方法的实施非常高效，并且所有检索到的子结构在节点和分支数量以及曲率程度上都与标记的子结构非常相似。
