Projektbeschreibung
Bessere Tröpfchenverfolgung auf Mikroebene
Die Fertigung in den Biowissenschaften erfordert häufig die Handhabung winziger Flüssigkeitsmengen. Die derzeit eingesetzten Methoden zur Analyse solcher Mikrofluidik können jedoch viel Zeit in Anspruch nehmen und die Produktionskosten erheblich erhöhen. Das EU-finanzierte Projekt DROPTRACK will mithilfe maschinellen Sehens eine zuverlässige und kostengünstige Möglichkeit schaffen, solche Mikrofluide zu verfolgen und zu analysieren. Mit zwei Deep-Learning-Algorithmen wird die DROPTRACK-Software in der Lage sein, einzelne Tröpfchen aus Versuchsvideos zu erkennen und zu verfolgen und physikalische Größen wie Tröpfchenzahl, Durchflussrate und Packungsanteil zu messen. Vorläufige Ergebnisse deuten an, dass die Software Bilder viel schneller analysieren kann als eine herkömmliche Digitalkamera, womit Nutzende in biologischen Bereichen eine erhebliche Zeitersparnis erzielen könnten.
Ziel
Microfluidics technology targets droplet manipulation by confining fluids to manufacture materials for many industrial applications and life sciences. Data analysis obtained by microfluidic experiments remains a bottleneck to this day due to the lack of a reliable interface converting raw observations to informative data fast enough and at low operating costs. Many computer vision tools exist, which permit to infer useful physical information from video data but they typically target specific physical systems and lack broad applicability. Moreover, the existing tools need intensive development and fine-tuning before they can be useful in a specific scenario. Thus, such a lack of computer vision tools with broad applicability hinders the penetration of this technology for extensive microfluidics applications.
This PoC aims to develop a stand-alone, easy-to-use software (DropTrack) that can detect and track individual droplets from experimental videos. Two cutting-edge deep-learning algorithms, YOLO for droplet detection and DeepSORT for droplet tracking, will be at the software's core. DropTrack will provide trajectories of the individual droplets and measure physical quantities of interest, such as the droplet numbers, flow rates, packing fraction, etc., by analyzing videos from experiments. Our preliminary work indicates that DropTrack is capable of analyzing images much faster than the image capture rate of a typical digital camera, thereby opening entirely new prospects for real-time feedback control in experiments.
The realization of DropTrack will lead to dramatic time and resources savings in video data analysis of deformable moving objects in microfluidics. Beyond this PoC, the successful demonstration of this technology in microfluidics is expected to attract significant attention as a handy data analysis tool in other relevant fields such as multi-organism biological systems from cell aggregates to animal congregation.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencesdata science
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsmicrofluidics
- engineering and technologyother engineering and technologiesfood technology
- engineering and technologymaterials engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-AG-LS - HORIZON Lump Sum GrantGastgebende Einrichtung
16163 Genova
Italien