Grazing ruminants are exposed to gastrointestinal parasites, whose spread and diffusion may lead to clinical disease. Among all, gastrointestinal nematode infections are a common constraint in pasture-based herds and can cause a decrease in animal health, productivity and farm profitability. Thus, detecting, identifying and quantifying the presence and spread of the intestinal parasite infection is a crucial, but non-trivial task. Indeed, the diagnosis of intestinal parasite infection is performed with non-invasive tools, such as the microscopic examination of fecal samples. However, the manual counting of parasite eggs requires relatively costly microscopes and a highly trained observer, who has to stay focused on the task for several hours, often resulting in counting errors that lead to the prescription and use of inadequate dosage of drugs. This, together with the growing number of farms and veterinary centers requiring such operation, is supporting the research and development of automatic systems for the localization, identification and counting of fecal eggs.
The Kubic FLOTAC Microscope (KFM) is a compact, low-cost, versatile and portable digital microscope designed to autonomously analyse faecal specimens prepared with FLOTAC or Mini-FLOTAC, in both field and laboratory settings, for different parasites and hosts. Having been proven to acquire images comparable with the view provided by the traditional optical microscopes, the KFM is able to autonomously scan and acquire images from a FLOTAC or Mini-FLOTAC in a few minutes, allowing the operator to focus on a different task.
The aim of this challenge is to perform faecal egg detection of gastrointestinal nematodes (GINs) in cattle, based on RGB images acquired by using a KFM. The competition is intended to spread this topic to the scientific community of researchers in pattern recognition and image processing, as we strongly believe that their knowledge and expertise can benefit the faecal parassites detection process.
Post-doc researcher at University of Naples Federico II, Department of Veterinary Medicine and Animal Production
Electronic Engineer
Full professor at University of Naples Federico II, Department of Veterinary Medicine and Animal Production
Ph.D. student at University of Naples Federico II, Department of Electrical Engineering and Information Technologies
Research Fellow at University of Naples Federico II, Department of Electrical Engineering and Information Technologies. This is the Principal Investigator for the Challenge
Associate professor at University of Naples Federico II, Department of Veterinary Medicine and Animal Production
Full professor at University of Naples Federico II, Department of Veterinary Medicine and Animal Production
Full professor at University of Naples Federico II, Department of Electrical Engineering and Information Technologies