Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect and analyze samples on a miniaturized size-level. Fluid flows evolving in channels of only several tens to hundreds of micrometers in size are often of a 3D nature, affecting the tailored transport of cells and particles. To analyze flow phenomena and local distributions of particles within those channels, astigmatic particle tracking velocimetry (APTV) has become a valuable tool, on condition that basic requirements like low optical aberrations and particles with a very narrow size distribution are fulfilled. Making use of the progress made in the field of machine vision, deep neural networks may help to overcome these limiting requirements, opening new fields of applications for APTV and allowing them to be used by nonexpert users. To qualify the use of a cascaded deep convolutional neural network (CNN) for particle detection and position regression, a detailed investigation was carried out starting from artificial particle images with known ground truth to real flow measurements inside a microchannel, using particles with uni- and bimodal size distributions. In the case of monodisperse particles, the mean absolute error and standard deviation of particle depth-position of less than and about 1 [my]m were determined, employing the deep neural network and the classical evaluation method based on the minimum Euclidean distance approach. While these values apply to all particle size distributions using the neural network, they continuously increase towards the margins of the measurement volume of about one order of magnitude for the classical method, if nonmonodisperse particles are used. Nevertheless, limiting the depth of measurement volume in between the two focal points of APTV, reliable flow measurements with low uncertainty are also possible with the classical evaluation method and polydisperse tracer particles. The results of the flow measurements presented herein confirm this finding. The source code of the deep neural network used here is available on https://github.com/SECSY-Group/DNN-APTV.

中文翻译：

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使用级联卷积神经网络进行像散 PTV 三维流量测量

化学、生物学和医学中的许多应用都使用微流体装置来分离、检测和分析小型化尺寸的样品。在只有几十到几百微米大小的通道中演化的流体流动通常具有 3D 性质，影响细胞和颗粒的定制传输。为了分析这些通道内粒子的流动现象和局部分布，像散粒子跟踪测速 (APTV) 已成为一种有价值的工具，条件是满足低光学像差和具有非常窄尺寸分布的粒子等基本要求。利用机器视觉领域取得的进展，深度神经网络可能有助于克服这些限制要求，为 APTV 开辟新的应用领域，并允许非专家用户使用它们。为了验证级联深度卷积神经网络 (CNN) 在粒子检测和位置回归中的使用，进行了详细的调查，从具有已知地面实况的人工粒子图像到微通道内的真实流量测量，使用具有单向和双峰尺寸分布。在单分散粒子的情况下，使用深度神经网络和基于最小欧几里德距离方法的经典评估方法，确定了小于和大约 1 μm 的粒子深度位置的平均绝对误差和标准偏差。虽然这些值适用于使用神经网络的所有粒度分布，但对于经典方法，它们不断向测量体积的边缘增加大约一个数量级，如果使用非单分散颗粒。尽管如此，通过限制 APTV 的两个焦点之间的测量体积深度，使用经典评估方法和多分散示踪粒子也可以实现具有低不确定性的可靠流量测量。此处提供的流量测量结果证实了这一发现。此处使用的深度神经网络的源代码可在 https://github.com/SECSY-Group/DNN-APTV 上获得。