Ross James D, Cullen D Kacy, Harris James P, LaPlaca Michelle C, DeWeerth Stephen P
Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory Atlanta, GA, USA ; School of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA, USA.
Department of Neurosurgery, University of Pennsylvania Philadelphia, PA, USA ; Philadelphia Veterans Affairs Medical Center Philadelphia, PA, USA.
Front Neuroanat. 2015 Jul 20;9:87. doi: 10.3389/fnana.2015.00087. eCollection 2015.
Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions.
三维（3-D）图像分析技术为快速、准确地评估神经细胞之间复杂的形态和功能相互作用提供了有力手段。当前基于软件的神经细胞识别方法一般分为两种应用：（1）在高密度结构中分割细胞核，或（2）在单细胞研究中追踪细胞神经突。我们开发了新颖的方法，以系统地识别在厚组织或三维体外构建体中具有丰富形态细节和密集神经突分支的神经元胞体群体。图像分析结合了几种新颖的自动特征，通过最初在二维中对特征进行分类并将这些分类合并为三维对象来区分神经突和胞体；三维重建会自动识别并校正分割过度和分割不足的错误。此外，该平台还提供软件辅助的错误校正，以进一步将错误最小化。这些特征能够实现非常准确的细胞边界识别，以处理各种形态复杂性。我们使用来自厚三维神经构建体的共聚焦z-stack验证了这些工具，其中神经元胞体具有不同程度的神经突分支和复杂性，准确率达到了≥95%。我们通过对离体脑切片中的神经细胞进行自动分割，在一个更复杂的领域展示了这些算法的稳健性。这些新方法通过以下方式提高了稳健性和准确性，从而超越了以前的技术：（1）处理神经突和胞体的能力，（2）双向分割校正，以及（3）通过软件辅助用户输入进行验证。这个三维图像分析平台为在三维环境中对神经组织或组织替代物进行无偏分析提供了有价值的工具，适用于研究多维细胞-细胞和细胞-细胞外基质相互作用。
