Authors
Quentin Chaboche, Isabelle Bonnet, Carles Blanch-Mercader
Published in
Soft matter. Sep 21, 2026. Epub Sep 21, 2026.
Abstract
Here, we present an open Python-based program with a graphical interface to characterise topological defects from two-dimensional time-lapses. This program computes the nematic orientation field, identifies defect positions, topological charges, and defect orientations, and estimates the splay-bend anisotropy parameter. The workflow is controlled by three detection parameters adjustable through the interface: the feature size, the order threshold and the phase contour radius. Validation on synthetic datasets with known ground truth yields less than 0.2% false positives and negatives and positional errors comparable to the resolution scale, and the absolute error of the splay-bend anisotropy is 0.03. In addition, we explain a correlation between the detected and input splay-bend anisotropy and provide a calibration curve. Next, we demonstrate applicability across biological systems spanning multiple scales. DeftPunk provides a versatile tool to characterise topological defects and infer elastic anisotropies from microscopy images, opening the way to systematic and quantitative investigations of active nematic dynamics in living systems.
PMID:
42764821
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.
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