Parasitic Egg Detection and Classification in Microscopic Images

Abstract

Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited. Targeting to automate routine faecal examination for parasitic diseases, this challenge aims to gather experts in the field to develop robust automated methods to detect and classify eggs of parasitic worms in a variety of microscopic images. Participants will work with a large-scale dataset, containing 11 types of parasitic eggs from faecal smear samples. They are the main interest because of causing major diseases and illness in developing countries. We open to any techniques used for parasitic egg recognition, ranging from conventional approaches based on statistical models to deep learning techniques. Finally, the organisers expect a new collaboration come out from the challenge.

Instructions:

Datasets contain 11 parasitic egg types. Each category has 1,000 images.

category_id 0: Ascaris lumbricoides
category_id 1: Capillaria philippinensis
category_id 2: Enterobius vermicularis
category_id 3: Fasciolopsis buski
category_id 4: Hookworm egg
category_id 5: Hymenolepis diminuta
category_id 6: Hymenolepis nana
category_id 7: Opisthorchis viverrine
category_id 8: Paragonimus spp
category_id 9: Taenia spp. egg
category_id 10: Trichuris trichiura

Please visit the Challenge Homepage (https://icip2022challenge.piclab.ai/).

Results must be submitted to the Leaderboard at the Challenge Homepage (https://icip2022challenge.piclab.ai/submission/).

Please cite our paper for the usage after the competition: N. Anantrasirichai, T. H. Chalidabhongse, D. Palasuwan, K. Naruenatthanaset, T. Kobchaisawat, N. Nunthanasup, K. Boonpeng, X. Ma and A. Achim, "ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results," IEEE ICIP2022.

Comments

Request for Access to Parasitic Egg Detection Dataset

Permalink Submitted by George Heazil on Mon, 09/23/2024 - 05:54

Dear IEEE DataPort Team,

I hope this message finds you well.

I am writing to request access to the dataset for the detection and classification of parasitic eggs in microscopic images, which is mentioned in the challenge on automating faecal examination for parasitic diseases. This dataset is critical for my research in developing automated methods to identify and classify eggs of parasitic worms, which is a significant contributor to diseases in developing countries.

Parasitic infections have been recognised as one of the major causes of illness worldwide, as highlighted by the WHO. The ability to automate the detection and classification of parasitic eggs in faecal smear samples could greatly enhance diagnostic capacities in resource-limited settings, where access to laboratory infrastructure may be limited.

As a researcher working in this domain, I am particularly interested in leveraging this dataset for deep learning techniques to develop robust models for parasitic egg recognition. I would greatly appreciate it if you could provide me with access to this dataset to further my research in this area.

Thank you for considering my request. I look forward to your response.

Best regards,
George Youhana