Ding Liya, Zhao Xuan, Guo Shuxia, Liu Yufeng, Liu Lijuan, Wang Yimin, Peng Hanchuan
Institute for Brain and Intelligence, Southeast University, Nanjing, China.
Guangdong Institute of Intelligence Science and Technology, Zhuhai, China.
Front Neuroinform. 2023 Jun 14;17:1174049. doi: 10.3389/fninf.2023.1174049. eCollection 2023.
Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons.
For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting.
Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.
神经元形态分析是神经元细胞类型定义的重要组成部分。形态重建是高通量形态分析工作流程中的一个瓶颈，由于密集神经元区域中的噪声和纠缠导致的错误额外重建限制了自动重建结果的可用性。我们提出了SNAP，一种基于结构的神经元形态重建修剪管道，通过减少错误的额外重建和分离纠缠的神经元来提高结果的可用性。
对于重建中四种不同类型的错误额外片段（由背景噪声、与附近神经元的树突纠缠、与其他神经元的轴突纠缠以及同一神经元内的纠缠引起），SNAP将特定的统计结构信息纳入错误额外片段检测规则，并实现修剪和多个树突分离。
实验结果表明，该管道以令人满意的精度和召回率完成了修剪。它还展示了良好的多个神经元分离性能。作为一种有效的重建后处理工具，SNAP可以促进神经元形态分析。
