Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.

树突形成输入和去极化的整合，控制神经系统中的神经元活动。神经元病变会破坏树突结构并导致损伤后的形态异常。枝晶再生可以通过各种参数进行量化，包括使用手动或自动图像分析方法的总枝晶长度和枝晶分支的数量。然而，手动量化是乏味且耗时的，并且自动化方法通常使用野生型神经元进行训练，使其不适合分析基因操纵或受伤的树突乔木。在这项研究中，我们测试了自动化图像分析软件在 IV 类果蝇上的表现如何神经元，有数百个单独的树突分支。我们应用每个软件来自动量化未受伤神经元和受伤后再生新树突的神经元的特征。再生的乔木在枝晶形态的多个特征中表现出缺陷，这使得它们对自动化管道进行分析具有挑战性。我们使用 ImageJ 中的 Simple Neurite Tracer 将三个自动化管道的性能与手动量化进行了比较：一个是市售的 (Imaris)，另一个是由独立研究小组开发的 (DeTerm 和 Tireless Tracing Genie)。在测试的三个软件中，我们确定 Imaris 在重建枝晶结构方面是最有效的，但即使经过大量手动编辑也不能准确测量总枝晶长度。Imaris 在计算树突分支方面优于 DeTerm 和 Tireless Tracing Genie，并且能够更好地从同一数据集重新创建先前的结论。这种对每个软件优缺点的彻底比较证明了它们在未来研究中分析再生神经元表型的实用性。