FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.
Med Image Anal
; 91: 103036, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-38016388
ABSTRACT
Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this process, however, high-performance algorithms have not been developed. The limitations in image acquisition, especially for intracellular parasites, make this process complex. For this reason, traditional image-processing methods are not easily transferred between different datasets and segmentation-based strategies do not have a high performance. Here, we propose a novel method FiCRoN, based on fully convolutional regression networks (FCRNs), as a promising new tool for estimating intracellular parasite burden. This estimation requires three values, intracellular parasites, infected cells and uninfected cells. FiCRoN solves this problem as multi-task learning counting by regression at two scales, a smaller one for intracellular parasites and a larger one for host cells. It does not use segmentation or detection, resulting in a higher generalization of counting tasks and, therefore, a decrease in error propagation. Linear regression reveals an excellent correlation coefficient between manual and automatic methods. FiCRoN is an innovative freedom-respecting image analysis software based on deep learning, designed to provide a fast and accurate quantification of parasite burden, also potentially useful as a single-cell counter.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Parasitos
/
Aprendizado Profundo
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Med Image Anal
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Colômbia
País de publicação:
Holanda