Automated identification of flagella from videomicroscopy via the medial axis transform

TL;DR

This paper introduces a fully automated, high-accuracy method for identifying and tracking flagella in videomicroscopy, eliminating manual input and heuristic reliance, thus enabling efficient analysis of flagellar motion.

Contribution

The authors present a novel automated algorithm based on the medial axis transform that accurately identifies flagella in microscopy videos without manual intervention.

Findings

High accuracy in flagella detection demonstrated on Leishmania mexicana videos

Achieves remarkable throughput with an unsupervised method

Comparable quality results to previous manual or heuristic-based approaches

Abstract

Ubiquitous in eukaryotic organisms, the flagellum is a well-studied organelle that is well-known to be responsible for motility in a variety of organisms. Commonly necessitated in their study is the capability to image and subsequently track the movement of one or more flagella using videomicroscopy, requiring digital isolation and location of the flagellum within a sequence of frames. Such a process in general currently requires some researcher input, providing some manual estimate or reliance on an experiment-specific heuristic to correctly identify and track the motion of a flagellum. Here we present a fully-automated method of flagellum identification from videomicroscopy based on the fact that the flagella are of approximately constant width when viewed by microscopy. We demonstrate the effectiveness of the algorithm by application to captured videomicroscopy of Leishmania mexicana, a parasitic monoflagellate of the family Trypanosomatidae. ImageJ Macros for flagellar identification are provided, and high accuracy and remarkable throughput are achieved via this unsupervised method, obtaining results comparable in quality to previous studies of closely-related species but achieved without the need for precursory measurements or the development of a specialised heuristic, enabling in general the automated generation of digitised kinematic descriptions of flagellar beating from videomicroscopy.