Wang Shih-Luen, Kahaki Seyed M M, Stepanyants Armen
Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University, Boston, MA 02115, USA.
Proc SPIE Int Soc Opt Eng. 2019 Feb;10949. doi: 10.1117/12.2512989. Epub 2019 Mar 15.
The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and neurological disorders. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: () neurites can appear broken due to inhomogeneous labeling and () neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (ANN) architectures and conventional image enhancement filters with the aim of alleviating both problems. We developed four image quality metrics to evaluate the effects of the proposed filters: normalized intensity in the cross-over regions between neurites, effective radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that ANN-based filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing the effective radius of neurites to nearly 1 voxel. In addition, this filter significantly decreases intensity in the cross-over regions between neurites and reduces fluctuations of intensity on neurites' centerlines. These results suggest that including an ANN-based filtering step, which does not require substantial extra time or computing power, can be beneficial for automated neuron tracing projects.
从显微镜图像中提取标记神经元的准确形态，对于绘制脑连接图谱以及理解学习和神经疾病背后的连接变化至关重要。然而，神经元光学显微镜成像存在两个特定问题，这使得准确的神经元追踪极具挑战性：（1）由于标记不均匀，神经突可能看起来是断裂的；（2）由于分辨率有限，神经突在三维空间中可能看起来是融合的。在此，我们提出并评估了几种人工神经网络（ANN）架构和传统图像增强滤波器，旨在缓解这两个问题。我们开发了四种图像质量指标来评估所提出滤波器的效果：神经突交叉区域的归一化强度、神经突的有效半径、沿神经突强度的变异系数以及局部背景与神经突强度之比。我们的结果表明，基于在优化的神经突半自动追踪上训练的ANN滤波器，能够显著优于传统滤波器。特别是，基于U-Net的滤波几乎可以消除背景强度，同时还能将神经突的有效半径减小到接近1个体素。此外，该滤波器显著降低了神经突交叉区域的强度，并减少了神经突中心线上强度的波动。这些结果表明，包含一个不需要大量额外时间或计算能力的基于ANN的滤波步骤，对于自动化神经元追踪项目可能是有益的。
