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Effective residual convolutional neural network for Chagas disease parasite segmentation

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Abstract

Considered a neglected tropical pathology, Chagas disease is responsible for thousands of deaths per year and it is caused by the parasite Trypanosoma cruzi. Since many infected people can remain asymptomatic, a fast diagnosis is necessary for proper intervention. Parasite microscopic observation in blood samples is the gold standard method to diagnose Chagas disease in its initial phase; however, this is a time-consuming procedure, requires expert intervention, and there is currently no efficient method to automatically perform this task. Therefore, we propose an efficient residual convolutional neural network, named Res2Unet, to perform a semantic segmentation of Trypanosoma cruzi parasites, with an active contour loss and improved residual connections, whose design is based on Heun’s method for solving ordinary differential equations. The model was trained on a dataset of 626 blood sample images and tested on a dataset of 207 images. Validation experiments report that our model achieved a Dice coefficient score of 0.84, a precision value of 0.85, and a recall value of 0.82, outperforming current state-of-the-art methods. Since Chagas disease is a severe and silent illness, our computational model may benefit health care providers to give a prompt diagnose for this worldwide affection.

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Data Availability

The dataset used in this work is available under request to the authors.

Code Availability

The source code is available under request to the authors.

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Funding

This scientific work was partly supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) of Mexico.

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Authors and Affiliations

  1. Computational Learning and Imaging Research (CLIR), Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tab. Cat. 13615, Merida, 97119, Mexico

    Allan Ojeda-Pat, Anabel Martin-Gonzalez, Carlos Brito-Loeza & Daniel Ruz-Suarez

  2. Centro de Investigaciones Regionales, Universidad Autónoma de Yucatán, Merida, 97000, Mexico

    Hugo Ruiz-Piña

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  1. Allan Ojeda-Pat
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Correspondence to Anabel Martin-Gonzalez.

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Ojeda-Pat, A., Martin-Gonzalez, A., Brito-Loeza, C. et al. Effective residual convolutional neural network for Chagas disease parasite segmentation. Med Biol Eng Comput 60, 1099–1110 (2022). https://doi.org/10.1007/s11517-022-02537-9

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  • DOI: https://doi.org/10.1007/s11517-022-02537-9

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