* Instituto de Ciências Exatas e Tecnológicas - Universidade Federal de Viçosa, 38810-000 Rio Paranaí, MG, Brazil.
† Department of Computer Science - UFSCar, 13565-905 S. Carlos, SP, Brazil.
Int J Neural Syst. 2015 Dec;25(8):1550033. doi: 10.1142/S0129065715500331. Epub 2015 Sep 14.
Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.
微电极阵列 (MEA) 是一种用于长期记录体外神经元培养物中细胞外自发或诱发活动的电生理记录设备。本工作提出并开发了一种用于 MEA 上神经元培养物的定量和形态分析的框架，通过处理其相应的荧光显微镜图像。通过使用阈值分割、分水岭变换和目标分类的组合，从荧光通道图像中分割出神经元。使用圆形霍夫变换从透射光通道图像中获取微电极的位置。所提出的方法应用于大鼠背根神经节 (DRG) 神经元细胞的分离培养图像。进行的形态和拓扑定量分析提供了有关培养状态的信息，例如群体计数、神经元-神经元和神经元-微电极之间的距离、胞体形态、神经元大小、神经元和微电极的空间分布。从 MEA 上的神经元培养物拍摄的显微镜图像的大多数分析仅考虑简单的定性分析。此外，所提出的框架旨在标准化图像处理，并计算用于集成图像-信号研究和进一步计算模拟的有用定量度量。结果表明，所实现的微电极识别方法具有鲁棒性，所实现的神经元分割和分类方法也是如此（分割正确率高达 84%）。该方法检索的定量信息与记录的电生理信号的集成信号-图像研究以及 MEA 上神经元培养的物理方面密切相关。虽然实验涉及 DRG 细胞图像，但也可以通过在图像处理参数估计中进行微小调整来处理皮质和海马细胞图像。
