{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T15:57:31Z","timestamp":1792166251170,"version":"4.4.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["RTI2018-094312-B-I00 (European FEDER funds)"],"award-info":[{"award-number":["RTI2018-094312-B-I00 (European FEDER funds)"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["FPI PRE2019-088214"],"award-info":[{"award-number":["FPI PRE2019-088214"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Skeletonization algorithms are used as basic methods to solve tracking problems, pose estimation, or predict animal group behavior. Traditional skeletonization techniques, based on image processing algorithms, are very sensitive to the shapes of the connected components in the initial segmented image, especially when these are low-resolution images. Currently, neural networks are an alternative providing more robust results in the presence of image-based noise. However, training a deep neural network requires a very large and balanced dataset, which is sometimes too expensive or impossible to obtain. This work proposes a new training method based on a custom-generated dataset with a synthetic image simulator. This training method was applied to different U-Net neural networks architectures to solve the problem of skeletonization using low-resolution images of multiple <jats:italic>Caenorhabditis elegans<\/jats:italic> contained in Petri dishes measuring 55\u00a0mm in diameter. These U-Net models had only been trained and validated with a synthetic image; however, they were successfully tested with a dataset of real images. All the U-Net models presented a good generalization of the real dataset, endorsing the proposed learning method, and also gave good skeletonization results in the presence of image-based noise. The best U-Net model presented a significant improvement of 3.32% with respect to previous work using traditional image processing techniques.<\/jats:p>","DOI":"10.1007\/s11263-023-01818-6","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T20:35:25Z","timestamp":1686256525000},"page":"2408-2424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Skeletonizing Caenorhabditis elegans Based on U-Net Architectures Trained with a Multi-worm Low-Resolution Synthetic Dataset"],"prefix":"10.1007","volume":"131","author":[{"given":"Pablo E.","family":"Layana Castro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Garc\u00eda Garv\u00ed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Navarro Moya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-5356","authenticated-orcid":false,"given":"Antonio-Jos\u00e9","family":"S\u00e1nchez-Salmer\u00f3n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"1818_CR1","unstructured":"Alexandre, M. (2019). Pytorch-unet. Code https:\/\/github.com\/milesial\/Pytorch-UNet."},{"key":"1818_CR2","doi-asserted-by":"publisher","unstructured":"Baheti, B., Innani, S., Gajre, S., et\u00a0al. (2020). Eff-unet: A novel architecture for semantic segmentation in unstructured environment. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, Seattle, pp. 1473\u20131481, https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00187.","DOI":"10.1109\/CVPRW50498.2020.00187"},{"issue":"9","key":"1818_CR3","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1007\/s11548-020-02203-1","volume":"15","author":"L Bargsten","year":"2020","unstructured":"Bargsten, L., & Schlaefer, A. (2020). Specklegan: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. International Journal of Computer Assisted Radiology and Surgery, 15(9), 1427\u20131436. https:\/\/doi.org\/10.1007\/s11548-020-02203-1","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"1818_CR4","doi-asserted-by":"publisher","unstructured":"Biron, D., Haspel, G. (eds) (2015) C . elegans. Springer Science+Business Media, New York. https:\/\/doi.org\/10.1007\/978-1-4939-2842-2","DOI":"10.1007\/978-1-4939-2842-2"},{"key":"1818_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/rs12071128","author":"K Cao","year":"2020","unstructured":"Cao, K., & Zhang, X. (2020). An improved res-unet model for tree species classification using airborne high-resolution images. Remote Sensing. https:\/\/doi.org\/10.3390\/rs12071128","journal-title":"Remote Sensing"},{"key":"1818_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L., Strauch, M., Daub, M., et\u00a0al (2020) A cnn framework based on line annotations for detecting nematodes in microscopic images. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, Iowa City, IA, USA, pp. 508\u2013512. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098465","DOI":"10.1109\/ISBI45749.2020.9098465"},{"issue":"4","key":"1818_CR7","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1007\/s11263-020-01412-0","volume":"129","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Ouyang, W., Liu, T., et al. (2021). A shape transformation-based dataset augmentation framework for pedestrian detection. International Journal of Computer Vision, 129(4), 1121\u20131138. https:\/\/doi.org\/10.1007\/s11263-020-01412-0","journal-title":"International Journal of Computer Vision"},{"key":"1818_CR8","unstructured":"Conn, P. M. (Ed.). (2017). Animal models for the study of human disease. Texas: Sara Tenney."},{"key":"1818_CR9","doi-asserted-by":"publisher","first-page":"97,228","DOI":"10.1109\/ACCESS.2021.3094201","volume":"9","author":"C Dewi","year":"2021","unstructured":"Dewi, C., Chen, R. C., Liu, Y. T., et al. (2021). Yolo v4 for advanced traffic sign recognition with synthetic training data generated by various gan. IEEE Access, 9, 97,228-97,242. https:\/\/doi.org\/10.1109\/ACCESS.2021.3094201","journal-title":"IEEE Access"},{"key":"1818_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21113893","author":"G Di Rosa","year":"2020","unstructured":"Di Rosa, G., Brunetti, G., Scuto, M., et al. (2020). Healthspan enhancement by olive polyphenols in C. elegans wild type and Parkinson\u2019s models. International Journal of Molecular Sciences. https:\/\/doi.org\/10.3390\/ijms21113893","journal-title":"International Journal of Molecular Sciences"},{"key":"1818_CR11","doi-asserted-by":"crossref","unstructured":"Doshi, K. (2019) Synthetic image augmentation for improved classification using generative adversarial networks. arXiv preprint arXiv:1907.13576.","DOI":"10.32555\/2019.dl.008"},{"key":"1818_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/s21144943","author":"A Garc\u00eda Garv\u00ed","year":"2021","unstructured":"Garc\u00eda Garv\u00ed, A., Puchalt, J. C., Layana Castro, P. E., et al. (2021). Towards lifespan automation for Caenorhabditis elegans based on deep learning: Analysing convolutional and recurrent neural networks for dead or live classification. Sensors. https:\/\/doi.org\/10.3390\/s21144943","journal-title":"Sensors"},{"issue":"1","key":"1818_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms9919","volume":"6","author":"JH Hahm","year":"2015","unstructured":"Hahm, J. H., Kim, S., DiLoreto, R., et al. (2015). C. elegans maximum velocity correlates with healthspan and is maintained in worms with an insulin receptor mutation. Nature Communications, 6(1), 1\u20137. https:\/\/doi.org\/10.1038\/ncomms9919","journal-title":"Nature Communications"},{"issue":"2","key":"1818_CR14","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s41095-019-0132-5","volume":"5","author":"L Han","year":"2019","unstructured":"Han, L., Tao, P., & Martin, R. R. (2019). Livestock detection in aerial images using a fully convolutional network. Computational Visual Media, 5(2), 221\u2013228. https:\/\/doi.org\/10.1007\/s41095-019-0132-5","journal-title":"Computational Visual Media"},{"issue":"4","key":"1818_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1008914","volume":"17","author":"L Hebert","year":"2021","unstructured":"Hebert, L., Ahamed, T., Costa, A. C., et al. (2021). Wormpose: Image synthesis and convolutional networks for pose estimation in C. elegans. PLOS Computational Biology, 17(4), 1\u201320. https:\/\/doi.org\/10.1371\/journal.pcbi.1008914","journal-title":"PLOS Computational Biology"},{"key":"1818_CR16","doi-asserted-by":"publisher","unstructured":"Hinterstoisser, S., Pauly, O., Heibel, H., et\u00a0al (2019) An annotation saved is an annotation earned: Using fully synthetic training for object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, Seoul, Korea (South), pp. 2787\u20132796. https:\/\/doi.org\/10.1109\/ICCVW.2019.00340","DOI":"10.1109\/ICCVW.2019.00340"},{"key":"1818_CR17","doi-asserted-by":"publisher","unstructured":"Huang, H., Lin, L., Tong, R., et\u00a0al (2020) Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Barcelona, Spain, pp. 1055\u20131059. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1818_CR18","unstructured":"Ioffe, S., Szegedy, C. (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: F. Bach, D. Blei (eds) Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol\u00a037. PMLR, Lille, France, pp. 448\u2013456"},{"key":"1818_CR19","unstructured":"Iqbal, H. (2018) Harisiqbal88\/plotneuralnet v1.0.0. Code https:\/\/github.com\/HarisIqbal88\/PlotNeuralNet."},{"issue":"2","key":"1818_CR20","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A., et al. (2021). nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203\u2013211. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nature Methods"},{"issue":"9","key":"1818_CR21","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1038\/s41592-018-0112-1","volume":"15","author":"A Javer","year":"2018","unstructured":"Javer, A., Currie, M., Lee, C. W., et al. (2018). An open-source platform for analyzing and sharing worm-behavior data. Nature Methods, 15(9), 645\u2013646. https:\/\/doi.org\/10.1038\/s41592-018-0112-1","journal-title":"Nature Methods"},{"key":"1818_CR22","doi-asserted-by":"publisher","unstructured":"Javer, A., Brown, A.E., Kokkinos, I., et\u00a0al. (2019). Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, vol 11134. Springer, Cham, pp. 0\u20130. https:\/\/doi.org\/10.1007\/978-3-030-11024-6_35","DOI":"10.1007\/978-3-030-11024-6_35"},{"issue":"1","key":"1818_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0084830","volume":"9","author":"SK Jung","year":"2014","unstructured":"Jung, S. K., Aleman-Meza, B., Riepe, C., et al. (2014). Quantworm: A comprehensive software package for Caenorhabditis elegans phenotypic assays. PLOS ONE, 9(1), 1\u20139. https:\/\/doi.org\/10.1371\/journal.pone.0084830","journal-title":"PLOS ONE"},{"issue":"6","key":"1818_CR24","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1038\/s41596-020-0321-9","volume":"15","author":"M Koopman","year":"2020","unstructured":"Koopman, M., Peter, Q., Seinstra, R. I., et al. (2020). Assessing motor-related phenotypes of Caenorhabditis elegans with the wide field-of-view nematode tracking platform. Nature protocols, 15(6), 2071\u20132106. https:\/\/doi.org\/10.1038\/s41596-020-0321-9","journal-title":"Nature protocols"},{"key":"1818_CR25","unstructured":"Koul, A., Ganju, S., Kasam, M. (2019). Practical Deep Learning for Cloud, Mobile and Edge: Real-World AI and Computer Vision Projects Using Python, Keras and TensorFlow. O\u2019Reilly Media, Incorporated. https:\/\/www.oreilly.com\/library\/view\/practical-deep-learning\/9781492034858\/"},{"issue":"1","key":"1818_CR26","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.devcel.2019.03.010","volume":"49","author":"S Kumar","year":"2019","unstructured":"Kumar, S., Egan, B. M., Kocsisova, Z., et al. (2019). Lifespan extension in C. elegans caused by bacterial colonization of the intestine and subsequent activation of an innate immune response. Developmental Cell, 49(1), 100-117.e6. https:\/\/doi.org\/10.1016\/j.devcel.2019.03.010","journal-title":"Developmental Cell"},{"issue":"1","key":"1818_CR27","doi-asserted-by":"publisher","first-page":"22,247","DOI":"10.1038\/s41598-020-79430-8","volume":"10","author":"PE Layana Castro","year":"2020","unstructured":"Layana Castro, P. E., Puchalt, J. C., & S\u00e1nchez-Salmer\u00f3n, A. J. (2020). Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Scientific Reports, 10(1), 22,247. https:\/\/doi.org\/10.1038\/s41598-020-79430-8","journal-title":"Scientific Reports"},{"key":"1818_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/s21165622","author":"PE Layana Castro","year":"2021","unstructured":"Layana Castro, P. E., Puchalt, J. C., Garc\u00eda Garv\u00ed, A., et al. (2021). Caenorhabditis elegans multi-tracker based on a modified skeleton algorithm. Sensors. https:\/\/doi.org\/10.3390\/s21165622","journal-title":"Sensors"},{"issue":"1","key":"1818_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-020-1013-2","volume":"3","author":"KN Le","year":"2020","unstructured":"Le, K. N., Zhan, M., Cho, Y., et al. (2020). An automated platform to monitor long-term behavior and healthspan in Caenorhabditis elegans under precise environmental control. Communications Biology, 3(1), 1\u201313. https:\/\/doi.org\/10.1038\/s42003-020-1013-2","journal-title":"Communications Biology"},{"issue":"4","key":"1818_CR30","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1109\/JBHI.2019.2946092","volume":"24","author":"H Li","year":"2020","unstructured":"Li, H., Fang, J., Liu, S., et al. (2020). Cr-unet: A composite network for ovary and follicle segmentation in ultrasound images. IEEE Journal of Biomedical and Health Informatics, 24(4), 974\u2013983. https:\/\/doi.org\/10.1109\/JBHI.2019.2946092","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"1818_CR31","doi-asserted-by":"publisher","unstructured":"Li, S., G\u00fcnel, S., Ostrek, M., et\u00a0al. (2020b) Deformation-aware unpaired image translation for pose estimation on laboratory animals. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Seattle, WA, USA, pp. 13155\u201313165. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01317","DOI":"10.1109\/CVPR42600.2020.01317"},{"issue":"10","key":"1818_CR32","doi-asserted-by":"publisher","first-page":"3064","DOI":"10.1109\/TMI.2020.2986781","volume":"39","author":"X Liu","year":"2020","unstructured":"Liu, X., Zhou, T., Lu, M., et al. (2020). Deep learning for ultrasound localization microscopy. IEEE Transactions on Medical Imaging, 39(10), 3064\u20133078. https:\/\/doi.org\/10.1109\/TMI.2020.2986781","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1818_CR33","doi-asserted-by":"publisher","unstructured":"Mais, L., Hirsch, P., Kainmueller, D. (2020). Patchperpix for instance segmentation. In: European Conference on Computer Vision, Springer, vol. 12370. Springer, Cham, pp. 288\u2013304. https:\/\/doi.org\/10.1007\/978-3-030-58595-2_18","DOI":"10.1007\/978-3-030-58595-2_18"},{"key":"1818_CR34","doi-asserted-by":"publisher","unstructured":"Mane, M. R., Deshmukh, A. A., Iliff A. J. (2020) Head and tail localization of C. elegans. arXiv preprint arXiv:2001.03981. https:\/\/doi.org\/10.48550\/arXiv.2001.03981","DOI":"10.48550\/arXiv.2001.03981"},{"key":"1818_CR35","doi-asserted-by":"publisher","unstructured":"Mayershofer, C., Ge, T., Fottner, J. (2021). Towards fully-synthetic training for industrial applications. In: LISS 2020. Springer, Singapore, pp. 765\u2013782. https:\/\/doi.org\/10.1007\/978-981-33-4359-7_53","DOI":"10.1007\/978-981-33-4359-7_53"},{"key":"1818_CR36","doi-asserted-by":"publisher","unstructured":"McManigle, J. E., Bartz, R. R., Carin, L. (2020). Y-net for chest x-ray preprocessing: Simultaneous classification of geometry and segmentation of annotations. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). IEEE, Montreal, QC, Canada, pp. 1266\u20131269. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9176334","DOI":"10.1109\/EMBC44109.2020.9176334"},{"key":"1818_CR37","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ejmp.2019.10.001","volume":"67","author":"S Moradi","year":"2019","unstructured":"Moradi, S., Oghli, M. G., Alizadehasl, A., et al. (2019). Mfp-unet: A novel deep learning based approach for left ventricle segmentation in echocardiography. Physica Medica, 67, 58\u201369. https:\/\/doi.org\/10.1016\/j.ejmp.2019.10.001","journal-title":"Physica Medica"},{"key":"1818_CR38","doi-asserted-by":"publisher","unstructured":"Olsen, A., Gill, M. S., (eds) (2017) Ageing: Lessons from C. elegans. Springer International Publishing, Switzerland. https:\/\/doi.org\/10.1007\/978-3-319-44703-2.","DOI":"10.1007\/978-3-319-44703-2"},{"issue":"1","key":"1818_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40317-021-00247-x","volume":"9","author":"C Padubidri","year":"2021","unstructured":"Padubidri, C., Kamilaris, A., Karatsiolis, S., et al. (2021). Counting sea lions and elephants from aerial photography using deep learning with density maps. Animal Biotelemetry, 9(1), 1\u201310. https:\/\/doi.org\/10.1186\/s40317-021-00247-x","journal-title":"Animal Biotelemetry"},{"key":"1818_CR40","doi-asserted-by":"publisher","unstructured":"Pashevich, A., Strudel, R., Kalevatykh, I., et\u00a0al (2019) Learning to augment synthetic images for sim2real policy transfer. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, Macau, China, pp. 2651\u20132657. https:\/\/doi.org\/10.1109\/IROS40897.2019.8967622.","DOI":"10.1109\/IROS40897.2019.8967622"},{"issue":"6","key":"1818_CR41","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1007\/s11357-019-00124-9","volume":"41","author":"JN Pitt","year":"2019","unstructured":"Pitt, J. N., Strait, N. L., Vayndorf, E. M., et al. (2019). Wormbot, an open-source robotics platform for survival and behavior analysis in C. elegans. GeroScience, 41(6), 961\u2013973. https:\/\/doi.org\/10.1007\/s11357-019-00124-9","journal-title":"GeroScience"},{"issue":"1","key":"1818_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-04854-3","volume":"12","author":"E Plebani","year":"2022","unstructured":"Plebani, E., Biscola, N. P., Havton, L. A., et al. (2022). High-throughput segmentation of unmyelinated axons by deep learning. Scientific Reports, 12(1), 1\u201316. https:\/\/doi.org\/10.1038\/s41598-022-04854-3","journal-title":"Scientific Reports"},{"issue":"4","key":"1818_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0215548","volume":"14","author":"JC Puchalt","year":"2019","unstructured":"Puchalt, J. C., S\u00e1nchez-Salmer\u00f3n, A. J., Martorell Guerola, P., et al. (2019). Active backlight for automating visual monitoring: An analysis of a lighting control technique for Caenorhabditis elegans cultured on standard petri plates. PLOS ONE, 14(4), 1\u201318. https:\/\/doi.org\/10.1371\/journal.pone.0215548","journal-title":"PLOS ONE"},{"key":"1818_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/s20215981","author":"JC Puchalt","year":"2020","unstructured":"Puchalt, J. C., Layana Castro, P. E., & S\u00e1nchez-Salmer\u00f3n, A. J. (2020). Reducing results variance in lifespan machines: An analysis of the influence of vibrotaxis on wild-type Caenorhabditis elegans for the death criterion. Sensors. https:\/\/doi.org\/10.3390\/s20215981","journal-title":"Sensors"},{"key":"1818_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-91898-6","author":"JC Puchalt","year":"2021","unstructured":"Puchalt, J. C., S\u00e1nchez-Salmer\u00f3n, A. J., Eugenio, I., et al. (2021). Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques. Scientific Reports. https:\/\/doi.org\/10.1038\/s41598-021-91898-6","journal-title":"Scientific Reports"},{"issue":"1","key":"1818_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-05823-6","volume":"12","author":"JC Puchalt","year":"2022","unstructured":"Puchalt, J. C., Gonzalez-Rojo, J. F., G\u00f3mez-Escribano, A. P., et al. (2022). Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard petri dishes. Scientific Reports, 12(1), 1\u201311. https:\/\/doi.org\/10.1038\/s41598-022-05823-6","journal-title":"Scientific Reports"},{"key":"1818_CR47","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.future.2019.11.021","volume":"108","author":"S Qamar","year":"2020","unstructured":"Qamar, S., Jin, H., Zheng, R., et al. (2020). A variant form of 3d-unet for infant brain segmentation. Future Generation Computer Systems, 108, 613\u2013623. https:\/\/doi.org\/10.1016\/j.future.2019.11.021","journal-title":"Future Generation Computer Systems"},{"key":"1818_CR48","doi-asserted-by":"publisher","unstructured":"Rizvandi, N. B., Pizurica, A., Philips, W. (2008a). Machine vision detection of isolated and overlapped nematode worms using skeleton analysis. In: 2008 15th IEEE International Conference on Image Processing. IEEE, San Diego, CA, USA, pp. 2972\u20132975. https:\/\/doi.org\/10.1109\/ICIP.2008.4712419","DOI":"10.1109\/ICIP.2008.4712419"},{"key":"1818_CR49","unstructured":"Rizvandi, N. B., Pi\u017eurica, A., Rooms, F., (2008b) Skeleton analysis of population images for detection of isolated and overlapped nematode C. elegans. In: 16th European Signal Processing Conference, pp. 1\u20135. Lausanne, Switzerland: IEEE."},{"key":"1818_CR50","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention, Springer, vol. 9351, pp. 234\u2013241. Cham: Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1818_CR51","doi-asserted-by":"publisher","unstructured":"Schraml, D. (2019). Physically based synthetic image generation for machine learning: a review of pertinent literature. In: Photonics and Education in Measurement Science 2019, International Society for Optics and Photonics, Jena, Germany, pp. 111440J. https:\/\/doi.org\/10.1117\/12.2533485.","DOI":"10.1117\/12.2533485"},{"key":"1818_CR52","doi-asserted-by":"publisher","unstructured":"Stiernagle, T. (2006). Maintenance of C. elegans. https:\/\/doi.org\/10.1895\/wormbook.1.101.1. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK19649\/?report=classic","DOI":"10.1895\/wormbook.1.101.1"},{"key":"1818_CR53","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.cmpb.2019.07.005","volume":"178","author":"P Tang","year":"2019","unstructured":"Tang, P., Liang, Q., Yan, X., et al. (2019). Efficient skin lesion segmentation using separable-unet with stochastic weight averaging. Computer Methods and Programs in Biomedicine, 178, 289\u2013301. https:\/\/doi.org\/10.1016\/j.cmpb.2019.07.005","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"1818_CR54","doi-asserted-by":"publisher","unstructured":"Trebing, K., Stanczyk, T., & Mehrkanoon, S. (2021). Smaat-unet: Precipitation nowcasting using a small attention-unet architecture. Pattern Recognition Letters, 145, 178\u2013186. https:\/\/doi.org\/10.1016\/j.patrec.2021.01.036","DOI":"10.1016\/j.patrec.2021.01.036"},{"key":"1818_CR55","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.compbiomed.2018.11.010","volume":"104","author":"P Tschandl","year":"2019","unstructured":"Tschandl, P., Sinz, C., & Kittler, H. (2019). Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation. Computers in Biology and Medicine, 104, 111\u2013116. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.11.010","journal-title":"Computers in Biology and Medicine"},{"key":"1818_CR56","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2202-8-86","author":"GD Tsibidis","year":"2007","unstructured":"Tsibidis, G. D., & Tavernarakis, N. (2007). Nemo: a computational tool for analyzing nematode locomotion. BMC Neuroscience. https:\/\/doi.org\/10.1186\/1471-2202-8-86","journal-title":"BMC Neuroscience"},{"key":"1818_CR57","doi-asserted-by":"publisher","unstructured":"Uhlmann, V., Unser, M. (2015) Tip-seeking active contours for bioimage segmentation. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, Brooklyn, NY, USA, pp. 544\u2013547. https:\/\/doi.org\/10.1109\/ISBI.2015.7163931.","DOI":"10.1109\/ISBI.2015.7163931"},{"key":"1818_CR58","doi-asserted-by":"publisher","unstructured":"Wang, D., Lu, Z., Bao, Z. (2019). Augmenting C. elegans microscopic dataset for accelerated pattern recognition. arXiv preprint arXiv:1906.00078. https:\/\/doi.org\/10.48550\/arXiv.1906.00078","DOI":"10.48550\/arXiv.1906.00078"},{"key":"1818_CR59","doi-asserted-by":"publisher","unstructured":"Wang, L., Kong, S., Pincus, Z., et\u00a0al. (2020). Celeganser: Automated analysis of nematode morphology and age. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, Seattle, WA, USA, pp. 4164\u20134173. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00492","DOI":"10.1109\/CVPRW50498.2020.00492"},{"key":"1818_CR60","doi-asserted-by":"publisher","unstructured":"Wiehman, S., de\u00a0Villiers, H. (2016). Semantic segmentation of bioimages using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, Vancouver, BC, Canada, pp. 624\u2013631, https:\/\/doi.org\/10.1109\/IJCNN.2016.7727258.","DOI":"10.1109\/IJCNN.2016.7727258"},{"key":"1818_CR61","doi-asserted-by":"publisher","unstructured":"Wiles, O., & Zisserman, A. (2019). Learning to predict 3d surfaces of sculptures from single and multiple views. International Journal of Computer Vision, 127(11), 1780\u20131800. https:\/\/doi.org\/10.1007\/s11263-018-1124-0","DOI":"10.1007\/s11263-018-1124-0"},{"key":"1818_CR62","doi-asserted-by":"publisher","first-page":"34859","DOI":"10.1038\/srep34859","volume":"6","author":"PB Winter","year":"2016","unstructured":"Winter, P. B., Brielmann, R. M., Timkovich, N. P., et al. (2016). A network approach to discerning the identities of C. elegans in a free moving population. Scientific Reports, 6, 34859. https:\/\/doi.org\/10.1038\/srep34859","journal-title":"Scientific Reports"},{"key":"1818_CR63","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1038\/nmeth.1984","volume":"9","author":"C W\u00f6hlby","year":"2012","unstructured":"W\u00f6hlby, C., Kamentsky, L., Liu, Z., et al. (2012). An image analysis toolbox for high-throughput C. elegans assays. Nature methods, 9, 714\u20136. https:\/\/doi.org\/10.1038\/nmeth.1984","journal-title":"Nature methods"},{"key":"1818_CR64","doi-asserted-by":"publisher","unstructured":"Yu, C. C. J., Raizen, D. M., & Fang-Yen, C. (2014). Multi-well imaging of development and behavior in Caenorhabditis elegans. Journal of Neuroscience Methods, 223, 35\u201339. https:\/\/doi.org\/10.1016\/j.jneumeth.2013.11.026","DOI":"10.1016\/j.jneumeth.2013.11.026"},{"key":"1818_CR65","doi-asserted-by":"publisher","first-page":"e66,410","DOI":"10.7554\/eLife.66410","volume":"10","author":"X Yu","year":"2021","unstructured":"Yu, X., Creamer, M. S., Randi, F., et al. (2021). Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training. eLife, 10, e66,410. https:\/\/doi.org\/10.7554\/eLife.66410","journal-title":"eLife"},{"key":"1818_CR66","doi-asserted-by":"publisher","DOI":"10.3390\/s19183859","author":"X Zhao","year":"2019","unstructured":"Zhao, X., Yuan, Y., Song, M., et al. (2019). Use of unmanned aerial vehicle imagery and deep learning unet to extract rice lodging. Sensors. https:\/\/doi.org\/10.3390\/s19183859","journal-title":"Sensors"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01818-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01818-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01818-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T03:34:41Z","timestamp":1690601681000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01818-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,8]]},"references-count":66,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1818"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01818-6","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,8]]},"assertion":[{"value":"24 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}