Chagas disease, also known as American Trypanosomiasis, was discovered in 1909 by Carlos Chagas, and is caused by the protozoan Trypanosoma cruzi (hereafter called T. cruzi). Accordingly to the World Health Organization (WHO) report from March 2013, approximately 7 to 8 million people worldwide are currently infected with T. cruzi and are at risk for developing cardiac or gut pathology normally associated with chronic Chagas disease. T. cruzi is transmitted to humans by infected triatomine bugs (known as ’kissing bugs’), which take blood meals from the inhabitants, and then defecate, leaving T. cruzi into wounds or mucosal sites. Although Chagas disease was once confined to the Americas, primarily Latin America, migration from endemic countries has led to the appearance of Chagas disease in nonendemic regions as well. Transmission of T. cruzi is possible through blood transfusion (or blood share), tissue transplantation, and also congenitally.
T. cruzi parasites may be detected in the bloodstream if the analysis is performed early, either by direct observation of blood or by various culture techniques, but unfortunately, the infection at this early stage often goes undetected because symptoms are nonspecific or absent. This microscopial detection might take about 30 minutes of careful looking by a specialist.
The main goal of this project is to explore image processing and analysis techniques for the recognition and counting of T. cruzi parasites in a thin blood smear, either in the trypomastigotes and amastigotes stage, saving this way a lot of time to specialized teams, and allowing the detection of the disease by non specialized teams. The images will be acquired with a generic microscope using the ‘Skylight’ smartphone-to-microscope adapter (requiring a magnification of 400x3), and will be analysed looking for T. cruzi parasites. It is expected that this analysis can be performed either in a central server as in a mobile platform.
Develop image processing and analysis techniques for the recognition, identification and counting of T. cruzi parasites. The developed image processing module should be able to count and correctly differentiate the T. cruzi in trypomastigote and amastigotes stages. It is expected that the analysis can be performed either in a central server as in a mobile platform.
For any additional information regarding this project, please contact us using the inquiries form.