RESUMEN
Chagas disease is a widely spreaded illness caused by the parasite Trypanosoma cruzi (T. cruzi). Most cases go unnoticed until the accumulated myocardial damage affect the patient. The endomyocardium biopsy is a tool to evaluate sustained myocardial damage, but analyzing histopathological images takes a lot of time and its prone to human error, given its subjective nature. The following work presents a deep learning method to detect T. cruzi amastigotes on histopathological images taken from a endomyocardium biopsy during an experimental murine model. A U-Net convolutional neural network architecture was implemented and trained from the ground up. An accuracy of 99.19% and Jaccard index of 49.43% were achieved. The obtained results suggest that the proposed approach can be useful for amastigotes detection in histopathological images.Clinical relevance- The proposed method can be incorporated as automatic detection tool of amastigotes nests, it can be useful for the Chagas disease analysis and diagnosis.