Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials

TL;DR

This paper introduces MED, a machine learning framework that automates the detection and characterization of topological defects in nematic textures across various systems, significantly improving speed and accuracy over traditional methods.

Contribution

The novel MED framework integrates advanced computer vision models to accurately identify and analyze topological defects in diverse nematic textures, including unseen defect types.

Findings

Achieves over 90% accuracy in defect recognition

Faster than conventional detection methods

Can identify unseen defect types

Abstract

Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of $\pm 1/2$ defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay--Berne particles. MED performs faster than conventional defect detection method and can achieve over 90\% accuracy on recognizing $\pm1/2$ defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED provides correct structural information about $\pm 1$ defects, i.e., the phase angle for $+1$ defects and the orientation angle for $-1$ defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.