title: ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.
abstract: Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.
authors.affiliation: University of Rome Tor Vergata, Department of Biology, via della Ricerca Scientifica 1, 00133 Rome, Italy; Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, 00133 Rome, Italy. Electronic address: emanuele.gandola@gmail.com.
authors.forename: Emanuele
authors.initials: E
authors.lastname: Gandola
authors.affiliation: University of Rome Tor Vergata, Department of Biology, via della Ricerca Scientifica 1, 00133 Rome, Italy; National Institute for Infectious Diseases 'L. Spallanzani' IRCCS, Via Portuense, 292 00149 Rome, Italy; Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Freiburg 79104, Germany.
authors.forename: Manuela
authors.initials: M
authors.lastname: Antonioli
authors.affiliation: The University of Manchester, Jodrell Bank Centre for Astrophysics, School of Physics and Astronomy, Manchester M13 9PL, UK.
authors.forename: Alessio
authors.initials: A
authors.lastname: Traficante
authors.affiliation: University of Rome Tor Vergata, Department of Biology, via della Ricerca Scientifica 1, 00133 Rome, Italy.
authors.forename: Simone
authors.initials: S
authors.lastname: Franceschini
authors.affiliation: University of Rome Tor Vergata, Department of Biology, via della Ricerca Scientifica 1, 00133 Rome, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Scardi
authors.affiliation: University of Rome Tor Vergata, Department of Biology, via della Ricerca Scientifica 1, 00133 Rome, Italy; AlgaRes, Spin off of University of Rome Tor Vergata, via della Ricerca Scientifica 1, 00133 Rome, Italy.
authors.forename: Roberta
authors.initials: R
authors.lastname: Congestri
mesh_terms: D000465:Algorithms; D001331:Automation / Q000295:instrumentation / Q000379:methods*; D000458:Cyanobacteria / Q000145:classification / Q000166:cytology* / Q000302:isolation & purification; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D005618:Fresh Water / Q000382:microbiology*; D007558:Italy; D000069550:Machine Learning; D008853:Microscopy / Q000295:instrumentation / Q000379:methods*
keywords: Algorithm; Bright field imaging; Cyanobacteria; Filamentous genera; Image analysis; Quantification