| Title | Automatic assessment of Leishmania infection indexes on in vitro macrophage cell cultures |
| Publication Type | Book |
| Year of Publication | 2012 |
| Authors | Leal, P, Ferro, L, Marques, M, Romão, S, Cruz, T, Tomá, AM, Castro, H, Quelhas, P |
| Series Title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci. |
| Volume | 7325 LNCS |
| Number of Pages | 432 - 439 |
| City | Aveiro |
| ISBN Number | 03029743 (ISSN); 9783642312977 (ISBN) |
| Keywords | Automated methods, Automatic assessment, Automatic evaluation, Automatic image analysis, Cell culture, Cell nuclei detection, cell nucleus, Colocalization, Digital image, Enzyme inhibition, Feed-back loop, Image analysis, Image filters, Image segmentation, In-vitro, Leishmania, Leishmania infantum, Macrophage cells, Macrophage culture, Macrophages, microscopy image segmentation, Microscopy images, Parasite infection, Parasite-, Size estimation, Visual inspection, Watershed segmentation |
| 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%. © 2012 Springer-Verlag. |
| URL | http://www.scopus.com/inward/record.url?eid=2-s2.0-84864131020&partnerID=40&md5=71523f4ff6de3e6ecc717c2fce28284a |
