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Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection

Esla Timothy Anzaku (UGent) , Mohammed Aliy Mohammed (UGent) , Utku Özbulak (UGent) , Jongbum Won, Hyesoo Hong, Janarthanan Krishnamoorthy, Sofie Van Hoecke (UGent) , Stefan Magez (UGent) , Arnout Van Messem (UGent) and Wesley De Neve (UGent)
(2023) SCIENTIFIC DATA. 10(1).
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Abstract
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.

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MLA
Anzaku, Esla Timothy, et al. “Tryp : A Dataset of Microscopy Images of Unstained Thick Blood Smears for Trypanosome Detection.” SCIENTIFIC DATA, vol. 10, no. 1, 2023, doi:10.1038/s41597-023-02608-y.
APA
Anzaku, E. T., Mohammed, M. A., Özbulak, U., Won, J., Hong, H., Krishnamoorthy, J., … De Neve, W. (2023). Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection. SCIENTIFIC DATA, 10(1). https://doi.org/10.1038/s41597-023-02608-y
Chicago author-date
Anzaku, Esla Timothy, Mohammed Aliy Mohammed, Utku Özbulak, Jongbum Won, Hyesoo Hong, Janarthanan Krishnamoorthy, Sofie Van Hoecke, Stefan Magez, Arnout Van Messem, and Wesley De Neve. 2023. “Tryp : A Dataset of Microscopy Images of Unstained Thick Blood Smears for Trypanosome Detection.” SCIENTIFIC DATA 10 (1). https://doi.org/10.1038/s41597-023-02608-y.
Chicago author-date (all authors)
Anzaku, Esla Timothy, Mohammed Aliy Mohammed, Utku Özbulak, Jongbum Won, Hyesoo Hong, Janarthanan Krishnamoorthy, Sofie Van Hoecke, Stefan Magez, Arnout Van Messem, and Wesley De Neve. 2023. “Tryp : A Dataset of Microscopy Images of Unstained Thick Blood Smears for Trypanosome Detection.” SCIENTIFIC DATA 10 (1). doi:10.1038/s41597-023-02608-y.
Vancouver
1.
Anzaku ET, Mohammed MA, Özbulak U, Won J, Hong H, Krishnamoorthy J, et al. Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection. SCIENTIFIC DATA. 2023;10(1).
IEEE
[1]
E. T. Anzaku et al., “Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection,” SCIENTIFIC DATA, vol. 10, no. 1, 2023.
@article{01HHPMM0WBGZBFBS7NHRC53SJS,
  abstract     = {{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.}},
  articleno    = {{716}},
  author       = {{Anzaku, Esla Timothy and Mohammed, Mohammed Aliy and Özbulak, Utku and Won, Jongbum and Hong, Hyesoo and Krishnamoorthy, Janarthanan and Van Hoecke, Sofie and Magez, Stefan and Van Messem, Arnout and De Neve, Wesley}},
  issn         = {{2052-4463}},
  journal      = {{SCIENTIFIC DATA}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{12}},
  title        = {{Tryp : a dataset of microscopy images of unstained thick blood smears for trypanosome detection}},
  url          = {{http://doi.org/10.1038/s41597-023-02608-y}},
  volume       = {{10}},
  year         = {{2023}},
}
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