Parasitic Egg Detection and Classification in Microscopic Images (ICIP 2022)

Associated SPS Event: IEEE ICIP 2022 Grand Challenge

Intestinal parasitic infections remain among the leading causes of morbidity worldwide, especially in tropical and sub-tropical areas with more temperate climates. According to WHO, approximately 1.5 billion people, or 24% of the world’s population, are infected with soil-transmitted helminth infections (STH), and 836 million children worldwide required preventive chemotherapy for STH in 2020. Most infections can cause diarrheal and other symptoms such as malnutrition and anaemia, particularly in children, who may suffer from growth failure. Most infected persons can also shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. An improvement in personal hygiene, better sanitation and a widespread health education campaign have reduced helminthic infections, but protozoa are still present even in asymptomatic hosts, leading to chronic diseases. In developing countries, intestinal protozoa and STH have been recognised as one of the most significant causes of illnesses. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory. However, this method shows low sensitivity, is time-consuming (30 min/sample), requires an experienced and skilled medical laboratory technologist and is impractical for use on-site. This means an automate routine faecal examination for parasitic diseases is essential.

This challenge aims to encourage and highlight novel strategies with a focus on robustness and accuracy in data-driven technologies to automatically detect parasitic eggs and also identify the egg type in compound microscopy images. We expect to gather experts in the fields of image processing, medical imaging and computer vision, which should not be limited to the domain of microscopic imaging as knowledge transfer can benefit the growth of the community. The outcome of the challenge could be further improved and assist diagnosis in real clinical use, or even automate detection and identification of intestinal parasite eggs, which can be used by non-experts. For further details, visit the Challenge page. Contact Nantheera Anantrasirichai, University of Bristol, United Kingdom, for more details.