Istanbul Medipol University, Department of Computer Engineering, Istanbul, Turkey.
Center for Neurodegeneration, Houston Methodist Research Institute, Houston, Texas, United States of America.
Sci Rep. 2020 Mar 20;10(1):5137. doi: 10.1038/s41598-020-61953-9.
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.
星形胶质细胞传统上被描述为被动支持细胞，但在过去十年的研究表明，它们在中枢神经系统生理和功能的许多方面都是活跃的参与者，无论是在正常状态还是疾病状态下。然而，调节星形胶质细胞功能及其在中枢神经系统内相互作用的确切机制仍知之甚少。造成这种知识差距的很大一部分原因是当前图像分析工具的局限性，这些工具不能有效地处理星形胶质细胞图像，也缺乏能够量化其复杂形态特征的方法。为了为星形胶质细胞荧光图像的定量分析提供一个无偏和准确的框架，我们引入了一种新的自动化图像处理管道，其主要新颖之处包括基于多尺度方向滤波器的细胞检测创新模块和利用深度学习和稀疏表示来减少训练数据需求并提高性能的分割例程。广泛的数值测试表明，我们的方法在具有挑战性的图像中与最先进的方法相比具有很强的竞争力，在这些图像中，星形胶质细胞聚集在一起。我们的代码是开源的，并免费提供给科学界。
