Title
Automatic assessment of leishmania infection indexes on in vitro macrophage cell cultures
Abstract
Evaluation of parasite infection indexes on in vitro cell cultures is a practice commonly employed by biomedical researchers to address biological questions or to test the efficacy of novel anti-parasitic compounds. In the particular case of Leishmania infantum, a unicellular parasite that parasitizes macrophages, infection indexes are usually determined either by visual inspection of cells directly under the microscope or by counting digital images using appropriate software. In either case assessment of infection indexes is time consuming, thus motivating the creation of automatic image analysis approaches that allow large scale studies of Leishmania-infected macrophage cultures. We propose a fully automated method for automatic evaluation of parasite infection indexes through the segmentation of individual macrophages nucleus and cytoplasm, as well as the segmentation and co-localization of the parasites in the image. To perform such analysis with robustness and increased performance we propose the use of local image filters tuned to the specific size of the objects to detect, in conjunction with image segmentation approaches. The objects size estimation is then improved through a learning feedback loop. Cytoplasm is detected by seeded watershed segmentation. Our approach obtains, for 86 images from 4 experiments, an average parasite infection index evaluation error of 2.3%.
Year
DOI
Venue
2012
10.1007/978-3-642-31298-4_51
ICIAR (2)
Keywords
Field
DocType
infection index,digital image,average parasite infection index,local image,automatic evaluation,vitro macrophage cell culture,watershed segmentation,automatic assessment,image segmentation approach,leishmania infection index,automatic image analysis approach,parasite infection index,unicellular parasite
Cell culture,Computer vision,Leishmania,Pattern recognition,Computer science,Segmentation,In vitro,Digital image,Robustness (computer science),Image segmentation,Artificial intelligence,Leishmania infantum
Conference
Volume
ISSN
Citations 
7325
0302-9743
3
PageRank 
References 
Authors
0.45
7
8
Name
Order
Citations
PageRank
Pedro Leal151.40
Luís Ferro230.78
Marco Marques371.68
Susana Romão430.78
Tânia Cruz530.78
Ana M. Tomá630.45
Helena Castro731.12
Pedro Quelhas826121.51