Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics

TL;DR

This paper demonstrates that deep learning-based optical flow significantly outperforms particle image velocimetry in measuring velocity fields in active nematic systems, especially at high densities, offering higher accuracy and resolution.

Contribution

The study evaluates and establishes deep learning optical flow as a superior method for quantifying flows in active nematics compared to traditional PIV, particularly in dense conditions.

Findings

DLOF outperforms PIV in dense samples

DLOF provides higher-resolution velocity fields in sparse samples

PIV struggles with contrast variations at high densities

Abstract

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.