Abstract
Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.
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Acknowledgments
This work has been developed in the framework of the project TEC2016-75976-R, financed by the Spanish Ministerio de Economía, Industria y Competitividad and the European Regional Development Fund (ERDF). We gratefully acknowledge the support of the Center for Cooperation and Development to the group of neglected diseases at UPC. We also give special thanks to Dr. Cristina Riera, Dr. Roser Fisa and Dr. Magdalena Alcover, from the parasitology Section of the Biology, Healthcare and the Environment Department of the Pharmacy Faculty at Universitat de Barcelona advising this work with their knowledge on the Leishmaniosi parasite. We thank the Characterization of Materials Group at UPC to let us use its microscope equipment. Finally we thank Sofia Melissa Limon Jacques for her related work during her Degree Project.
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Górriz, M., Aparicio, A., Raventós, B., Vilaplana, V., Sayrol, E., López-Codina, D. (2018). Leishmaniasis Parasite Segmentation and Classification Using Deep Learning. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2018. Lecture Notes in Computer Science(), vol 10945. Springer, Cham. https://doi.org/10.1007/978-3-319-94544-6_6
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