Systems Neuroscience and Neurotechnology Unit, Neurocenter, Faculty of Medicine, Saarland University & School of Engineering, htw saar, Germany.
Summer Program, Japan Society for the Promotion of Science (JSPS), Tokyo.
J Biophotonics. 2022 Aug;15(8):e202100330. doi: 10.1002/jbio.202100330. Epub 2022 May 15.
Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi-channel support and compatibility with existing 2-photon imaging suites.
功能 2 光子显微镜是一种用于成像神经元活动的关键技术。然而，记录的图像序列可能包含非刚性运动伪影，这需要高精度的运动校正。变分光流 (OF) 估计是一组用于运动分析的方法，在许多计算机视觉领域都具有良好的性能。然而，它尚未适应 2 光子神经成像数据的统计特性。在这项工作中，我们提出了运动补偿方法 Flow-Registration，它优于以前的对齐工具，甚至可以对齐和重建低信噪比的 2 光子成像数据，并能够补偿局部药物注射期间的高离散度位移。该方法基于此类数据的统计信息，并集成了变分 OF 估计的先前进展。我们的方法有一个易于使用的 ImageJ/FIJI 插件，以及一个 MATLAB 工具箱，具有模块化、面向对象的文件输入/输出、本机多通道支持以及与现有 2 光子成像套件的兼容性。
