Animals; Humans; Microscopy; Artificial Intelligence; Neglected Diseases; Trypanosomiasis, African/diagnosis; Trypanosomiasis, African/parasitology; Trypanosoma; Trypanosoma brucei brucei; Library and Information Sciences; Statistics, Probability and Uncertainty; Computer Science Applications; Education; Information Systems; Statistics and Probability
Abstract :
[en] Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.
Disciplines :
Biotechnology
Author, co-author :
Anzaku, Esla Timothy ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea. eslatimothy.anzaku@ugent.be ; IDLab, Ghent University, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium. eslatimothy.anzaku@ugent.be
Mohammed, Mohammed Aliy ; IDLab, Ghent University - imec, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium ; School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
Ozbulak, Utku; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
Won, Jongbum; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
Hong, Hyesoo; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
Krishnamoorthy, Janarthanan; School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
Van Hoecke, Sofie; IDLab, Ghent University - imec, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium
Magez, Stefan; Biomedical Research Center, Ghent University Global Campus, Incheon, 21985, South Korea ; Laboratory of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium ; Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
Van Messem, Arnout ; Université de Liège - ULiège > Département de mathématique > Statistique appliquée aux sciences
De Neve, Wesley ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea ; IDLab, Ghent University, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium
Language :
English
Title :
Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.
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