- -

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Layana-CastroPuch ...
Tamaño: 2.214Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Layana-Castro, Pablo Emmanuel es_ES
dc.contributor.author Puchalt-Rodríguez, Joan Carles es_ES
dc.contributor.author Sánchez Salmerón, Antonio José es_ES
dc.date.accessioned 2021-11-05T14:07:45Z
dc.date.available 2021-11-05T14:07:45Z
dc.date.issued 2020-12-17 es_ES
dc.identifier.issn 2045-2322 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176301
dc.description.abstract [EN] One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm. es_ES
dc.description.sponsorship This study was supported by the Plan Nacional de I+D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis S.L. and Archer Daniels Midland supplied the C. elegans plates. Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). Mrs. Maria-Gabriela Salazar-Secada developed the skeleton annotation application. Mr. Jordi Tortosa-Grau annotated worm skeletons. es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Scientific Reports es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Image processing es_ES
dc.subject Skeleton algorithm es_ES
dc.subject C. elegans es_ES
dc.subject Lifespan es_ES
dc.subject Healthspan es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Improving skeleton algorithm for helping Caenorhabditis elegans trackers es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41598-020-79430-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094312-B-I00/ES/MONITORIZACION AVANZADA DE COMPORTAMIENTOS DE CAENORHABDITIS ELEGANS, BASADA EN VISION ACTIVA, PARA ANALIZAR FUNCION COGNITIVA Y ENVEJECIMIENTO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P40 OD010440/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Layana-Castro, PE.; Puchalt-Rodríguez, JC.; Sánchez Salmerón, AJ. (2020). Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Scientific Reports. 10(1):1-12. https://doi.org/10.1038/s41598-020-79430-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41598-020-79430-8 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 1 es_ES
dc.identifier.pmid 33335258 es_ES
dc.identifier.pmcid PMC7746747 es_ES
dc.relation.pasarela S\434273 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
dc.description.references Teo, E. et al. A high throughput drug screening paradigm using transgenic Caenorhabditis elegans model of Alzheimer’s disease. Transl. Med. Aging 4, 11–21. https://doi.org/10.1016/j.tma.2019.12.002 (2020). es_ES
dc.description.references Kim, M., Knoefler, D., Quarles, E., Jakob, U. & Bazopoulou, D. Automated phenotyping and lifespan assessment of a C. elegans model of Parkinson’s disease. Transl. Med. Aging 4, 38–44. https://doi.org/10.1016/j.tma.2020.04.001 (2020). es_ES
dc.description.references Olsen, A. & Gill, M. S. (eds) Ageing: Lessons from C. elegans (Springer, Berlin, 2017). es_ES
dc.description.references Wählby, C. et al. An image analysis toolbox for high-throughput C. elegans assays. Nat. Methods 9, 714–6. https://doi.org/10.1038/nmeth.1984 (2012). es_ES
dc.description.references Rizvandi, N. B., Pižurica, A., Rooms, F. & Philips, W. Skeleton analysis of population images for detection of isolated and overlapped nematode C. elegans. In 2008 16th European Signal Processing Conference, 1–5 (2008). es_ES
dc.description.references Rizvandi, N. B., Pizurica, A. & Philips, W. Machine vision detection of isolated and overlapped nematode worms using skeleton analysis. In 2008 15th IEEE International Conference on Image Processing, 2972–2975. https://doi.org/10.1109/ICIP.2008.4712419 (2008). es_ES
dc.description.references Uhlmann, V. & Unser, M. Tip-seeking active contours for bioimage segmentation. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 544–547 (2015). es_ES
dc.description.references Nagy, S., Goessling, M., Amit, Y. & Biron, D. A generative statistical algorithm for automatic detection of complex postures. PLOS Comput. Biol. 11, 1–23. https://doi.org/10.1371/journal.pcbi.1004517 (2015). es_ES
dc.description.references Huang, K.-M., Cosman, P. & Schafer, W. R. Machine vision based detection of omega bends and reversals in C. elegans. J. Neurosci. Methods 158, 323–336. https://doi.org/10.1016/j.jneumeth.2006.06.007 (2006). es_ES
dc.description.references Kiel, M. et al. A multi-purpose worm tracker based on FIM. https://doi.org/10.1101/352948 (2018). es_ES
dc.description.references Winter, P. B. et al. A network approach to discerning the identities of C. elegans in a free moving population. Sci. Rep. 6, 34859. https://doi.org/10.1038/srep34859 (2016). es_ES
dc.description.references Fontaine, E., Burdick, J. & Barr, A. Automated tracking of multiple C. Elegans. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 3716–3719. https://doi.org/10.1109/IEMBS.2006.260657 (2006). es_ES
dc.description.references Roussel, N., Morton, C. A., Finger, F. P. & Roysam, B. A computational model for C. elegans locomotory behavior: application to multiworm tracking. IEEE Trans. Biomed. Eng. 54, 1786–1797. https://doi.org/10.1109/TBME.2007.894981 (2007). es_ES
dc.description.references Hebert, L., Ahamed, T., Costa, A. C., O’Shaugnessy, L. & Stephens, G. J. Wormpose: image synthesis and convolutional networks for pose estimation in C. elegans. bioRxiv. https://doi.org/10.1101/2020.07.09.193755 (2020). es_ES
dc.description.references Chen, L. et al. A CNN framework based on line annotations for detecting nematodes in microscopic images. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 508–512. https://doi.org/10.1109/ISBI45749.2020.9098465 (2020). es_ES
dc.description.references Li, S. et al. Deformation-aware unpaired image translation for pose estimation on laboratory animals. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13155–13165. https://doi.org/10.1109/CVPR42600.2020.01317 (2020). es_ES
dc.description.references Puchalt, J. C., Sánchez-Salmerón, A.-J., Martorell Guerola, P. & Genovés Martínez, S. Active backlight for automating visual monitoring: an analysis of a lighting control technique for Caenorhabditis elegans cultured on standard petri plates. PLOS ONE 14, 1–18. https://doi.org/10.1371/journal.pone.0215548 (2019). es_ES
dc.description.references Stiernagle, T. Maintenance of C. elegans. https://doi.org/10.1895/wormbook.1.101.1 (2006). es_ES
dc.description.references Russ, J. C. & Neal, F. B. The Image Processing Handbook 7th edn, 479–480 (CRC Press, Boca Raton, 2015). es_ES
dc.description.references Swierczek, N. A., Giles, A. C., Rankin, C. H. & Kerr, R. A. High-throughput behavioral analysis in C. elegans. Nat. Methods 8, 592–598. https://doi.org/10.1038/nmeth.1625 (2011). es_ES
dc.description.references Restif, C. et al. CELEST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion. PLOS Comput. Biol. 10, 1–12. https://doi.org/10.1371/journal.pcbi.1003702 (2014). es_ES
dc.description.references Javer, A. et al. An open-source platform for analyzing and sharing worm-behavior data. Nat. Methods 15, 645–646. https://doi.org/10.1038/s41592-018-0112-1 (2018). es_ES
dc.description.references Dusenbery, D. B. Using a microcomputer and video camera to simultaneously track 25 animals. Comput. Biol. Med. 15, 169–175. https://doi.org/10.1016/0010-4825(85)90058-7 (1985). es_ES
dc.description.references Ramot, D., Johnson, B. E., Berry, T. L. Jr., Carnell, L. & Goodman, M. B. The parallel worm tracker: a platform for measuring average speed and drug-induced paralysis in nematodes. PLOS ONE 3, 1–7. https://doi.org/10.1371/journal.pone.0002208 (2008). es_ES
dc.description.references Puchalt, J. C. et al. Improving lifespan automation for Caenorhabditis elegans by using image processing and a post-processing adaptive data filter. Sci. Rep. 10, 8729. https://doi.org/10.1038/s41598-020-65619-4 (2020). es_ES
dc.description.references Rezatofighi, H. et al. Generalized intersection over union: a metric and a loss for bounding box regression. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 658–666. https://doi.org/10.1109/CVPR.2019.00075 (2019). es_ES
dc.description.references Koul, A., Ganju, S. & Kasam, M. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow, 679–680 (O’Reilly Media, 2019). es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem