Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics
This work addresses the challenge of measuring fluid flows in active, soft, and biophysical systems, offering a versatile tool for researchers in these domains, though it is incremental as it applies an existing method to a new application.
The researchers tackled the problem of quantifying spontaneous flows in active nematics by comparing deep-learning optical flow (DLOF) with particle imaging velocimetry (PIV), finding that DLOF produces significantly more accurate velocity fields for densely labeled samples and higher-resolution fields for sparse samples.
Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects at the pixel level. In this article, we evaluate the ability of optical flow to quantify the spontaneous flows of MT-based active nematics under different labeling conditions. We compare DLOF against the commonly used technique, particle imaging velocimetry (PIV). We obtain flow velocity ground truths either by performing semi-automated particle tracking on samples with sparsely labeled filaments, or from passive tracer beads. We find that DLOF produces significantly more accurate velocity fields than PIV for densely labeled samples. We show that the breakdown of PIV arises because the algorithm cannot reliably distinguish contrast variations at high densities, particularly in directions parallel to the nematic director. DLOF overcomes this limitation. For sparsely labeled samples, DLOF and PIV produce results with similar accuracy, but DLOF gives higher-resolution fields. Our work establishes DLOF as a versatile tool for measuring fluid flows in a broad class of active, soft, and biophysical systems.