Effective residual convolutional neural network for Chagas disease parasite segmentation.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 4; pp. 1099 - 1111 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155874100&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155874100 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2022 vid: 60 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155874100 155874100 NLM35230611 10.1007/s11517-022-02537-9 NLM35230611 155874100 ppf: 1099 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effective residual convolutional neural network for Chagas disease parasite segmentation. aug: au: Ojeda-Pat, Allan Martin-Gonzalez, Anabel Brito-Loeza, Carlos Ruiz-Piña, Hugo Ruz-Suarez, Daniel affil: Computational Learning and Imaging Research (CLIR), Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tab. Cat. 13615, 97119, Merida, Mexico sug: subj: Parasites Trypanosomiasis Diagnosis Disease Progression Animals Image Processing, Computer Assisted Methods Clinical Assessment Tools ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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