{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:15:34Z","timestamp":1778818534373,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n            <jats:title>Abstract<\/jats:title>\n            <jats:p>The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1007\/s00371-024-03686-8","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T11:06:20Z","timestamp":1730804780000},"page":"4703-4722","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach"],"prefix":"10.1007","volume":"41","author":[{"given":"Julien","family":"Thomas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1828-7663","authenticated-orcid":false,"given":"Boyu","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6957-5174","authenticated-orcid":false,"given":"Yizhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5821-5766","authenticated-orcid":false,"given":"Stuart","family":"Barnes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4618-5786","authenticated-orcid":false,"given":"Karl","family":"Jenkins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"issue":"3","key":"3686_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.34257\/GJMBRAVOL20IS3PG25","volume":"20","author":"A Rajapaksha","year":"2020","unstructured":"Rajapaksha, A., Jayasuriya, N.: Smart airport: a review on future of the airport operation. Global J. Manag. Bus. Res. 20(3), 25\u201334 (2020)","journal-title":"Global J. Manag. Bus. Res."},{"key":"3686_CR2","unstructured":"UKRI, \u00a365 million future flight challenge phase 3 competition launches-ukri. https:\/\/www.ukri.org\/news\/65-million-future-flight-challenge-phase-3-competition-launches\/ (2021)"},{"key":"3686_CR3","unstructured":"UKRI, Out of cycle next generation highly efficient air transport (oneheart). https:\/\/gtr.ukri.org\/projects?ref=10003388 (2023)"},{"key":"3686_CR4","doi-asserted-by":"publisher","unstructured":"Zhao, J., Conrad, C., Delezenne, Q., Xu, Y., Tsourdos, A.: A digital twin mixed-reality system for testing future advanced air mobility concepts: a prototype. In: Integrated Communication. Navigation and Surveillance Conference (ICNS) 2023, pp. 1\u201310 (2023). https:\/\/doi.org\/10.1109\/ICNS58246.2023.10124310","DOI":"10.1109\/ICNS58246.2023.10124310"},{"key":"3686_CR5","doi-asserted-by":"publisher","unstructured":"Zhao, J., Li, Y.-G., Sampath, S.: Convolutional neural network denoising auto-encoders for intelligent aircraft engine gas path health signal noise filtering. J. Eng. Gas Turbin. Power 145(6) (2023). https:\/\/doi.org\/10.1115\/1.4056128","DOI":"10.1115\/1.4056128"},{"issue":"1\u20133","key":"3686_CR6","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neucom.2010.03.021","volume":"74","author":"J Misra","year":"2010","unstructured":"Misra, J., Saha, I.: Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1\u20133), 239\u2013255 (2010). https:\/\/doi.org\/10.1016\/j.neucom.2010.03.021","journal-title":"Neurocomputing"},{"issue":"12","key":"3686_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3578938","volume":"55","author":"G Menghani","year":"2023","unstructured":"Menghani, G.: Efficient deep learning: a survey on making deep learning models smaller, faster, and better. ACM Comput. Surv. 55(12), 1\u201337 (2023). https:\/\/doi.org\/10.1145\/3578938","journal-title":"ACM Comput. Surv."},{"key":"3686_CR8","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587 (2017)"},{"key":"3686_CR9","doi-asserted-by":"publisher","unstructured":"Kuang, B., Barnes, S., Tang, G., Jenkins, K.: A dataset for autonomous aircraft refueling on the ground (agr). In: 2023 28th International Conference on Automation and Computing (ICAC), pp. 55\u201360. IEEE. (2023) https:\/\/doi.org\/10.1109\/icac57885.2023.10275212","DOI":"10.1109\/icac57885.2023.10275212"},{"key":"3686_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109286","volume":"181","author":"G Kim","year":"2023","unstructured":"Kim, G., Choi, J.G., Ku, M., Lim, S.: Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing. Comput. Ind. Eng. 181, 109286 (2023). https:\/\/doi.org\/10.1016\/j.cie.2023.109286","journal-title":"Comput. Ind. Eng."},{"issue":"10","key":"3686_CR11","doi-asserted-by":"publisher","first-page":"7745","DOI":"10.1109\/JIOT.2020.3038848","volume":"8","author":"P Park","year":"2021","unstructured":"Park, P., Di Marco, P., Nah, J., Fischione, C.: Wireless avionics intracommunications: a survey of benefits, challenges, and solutions. IEEE Internet Things J. 8(10), 7745\u20137767 (2021). https:\/\/doi.org\/10.1109\/JIOT.2020.3038848","journal-title":"IEEE Internet Things J."},{"issue":"18","key":"3686_CR12","doi-asserted-by":"publisher","first-page":"3837","DOI":"10.3390\/math11183837","volume":"11","author":"S Khalid","year":"2023","unstructured":"Khalid, S., Song, J., Azad, M.M., Elahi, M.U., Lee, J., Jo, S.-H., Kim, H.S.: A comprehensive review of emerging trends in aircraft structural prognostics and health management. Mathematics 11(18), 3837 (2023). https:\/\/doi.org\/10.3390\/math11183837","journal-title":"Mathematics"},{"key":"3686_CR13","doi-asserted-by":"publisher","unstructured":"Faisal, N., Cora, O.N., Bekci, M.L., \u00c5\u0161liwa, R.E., Sternberg, Y., Pant, S., Degenhardt, R., Prathuru, A.: Defect Types, pp. 15\u201372 Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-72192-3_3","DOI":"10.1007\/978-3-030-72192-3_3"},{"key":"3686_CR14","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/978-3-030-47035-7_24","volume-title":"Aerospace Engineering","author":"H Hefazi","year":"2021","unstructured":"Hefazi, H.: Aerospace Engineering, pp. 1085\u20131137. Springer, Berlin (2021). https:\/\/doi.org\/10.1007\/978-3-030-47035-7_24"},{"key":"3686_CR15","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","volume":"304","author":"H Yu","year":"2018","unstructured":"Yu, H., Yang, Z., Tan, L., Wang, Y., Sun, W., Sun, M., Tang, Y.: Methods and datasets on semantic segmentation: a review. Neurocomputing 304, 82\u2013103 (2018)","journal-title":"Neurocomputing"},{"issue":"21","key":"3686_CR16","doi-asserted-by":"publisher","first-page":"30519","DOI":"10.1007\/s11042-022-12821-3","volume":"81","author":"U Sehar","year":"2022","unstructured":"Sehar, U., Naseem, M.L.: How deep learning is empowering semantic segmentation: traditional and deep learning techniques for semantic segmentation: A comparison. Multimedia Tools Appl. 81(21), 30519\u201330544 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"3686_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"3686_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, (2015), proceedings, part III 18, pp. 234\u2013241. Springer, (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"3686_CR19","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3686_CR20","unstructured":"Alexey, D.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv: 2010.11929 (2020)"},{"key":"3686_CR21","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3686_CR22","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2021.07.055","volume":"462","author":"MS Hossain","year":"2021","unstructured":"Hossain, M.S., Betts, J.M., Paplinski, A.P.: Dual focal loss to address class imbalance in semantic segmentation. Neurocomputing 462, 69\u201387 (2021)","journal-title":"Neurocomputing"},{"key":"3686_CR23","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge\u00a0Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA (2017), and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Qu\u00e9bec City, QC, Canada, September 14, Proceedings 3, pp. 240\u2013248. Springer (2017)","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"3686_CR24","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., Torresani, L.: Semantic segmentation with boundary neural fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3602\u20133610 (2016)","DOI":"10.1109\/CVPR.2016.392"},{"key":"3686_CR25","doi-asserted-by":"crossref","unstructured":"Hu, H., Cui, J., Wang, L.: Region-aware contrastive learning for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16291\u201316301 (2021)","DOI":"10.1109\/ICCV48922.2021.01598"},{"key":"3686_CR26","doi-asserted-by":"crossref","unstructured":"Chibane, J., Engelmann, F., Anh\u00a0Tran, T., Pons-Moll, G.: Box2mask: Weakly supervised 3d semantic instance segmentation using bounding boxes. In: European Conference on Computer Vision, pp. 681\u2013699. Springer (2022)","DOI":"10.1007\/978-3-031-19821-2_39"},{"key":"3686_CR27","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/b978-0-323-90198-7.00008-2","volume-title":"Transfer Learning","author":"S-C Huang","year":"2021","unstructured":"Huang, S.-C., Le, T.-H.: Transfer Learning, pp. 219\u2013233. Elsevier, New York (2021). https:\/\/doi.org\/10.1016\/b978-0-323-90198-7.00008-2"},{"key":"3686_CR28","doi-asserted-by":"publisher","first-page":"196197","DOI":"10.1109\/ACCESS.2020.3034343","volume":"8","author":"G Vrban\u010di\u010d","year":"2020","unstructured":"Vrban\u010di\u010d, G., Podgorelec, V.: Transfer learning with adaptive fine-tuning. IEEE Access 8, 196197\u2013196211 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"3686_CR29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.bbe.2021.11.004","volume":"42","author":"P Kora","year":"2022","unstructured":"Kora, P., Ooi, C.P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W.Y., Meenakshi, K., Swaraja, K., Plawiak, P., Acharya, U.R.: Transfer learning techniques for medical image analysis: a review. Biocybern. Biomed. Eng. 42(1), 79\u2013107 (2022)","journal-title":"Biocybern. Biomed. Eng."},{"key":"3686_CR30","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, J., Ma, J., Sun, H., Xu, Z., Zhang, T., Yu, H.: Deep transfer learning for intelligent vehicle perception: a survey. Green Energy Intell Transp 100125 (2023)","DOI":"10.1016\/j.geits.2023.100125"},{"key":"3686_CR31","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.jvcir.2019.05.002","volume":"62","author":"R Wang","year":"2019","unstructured":"Wang, R., Yao, X., Yang, J., Xue, L., Hu, M.: Hierarchical deep transfer learning for fine-grained categorization on micro datasets. J. Vis. Commun. Image Represent. 62, 129\u2013139 (2019)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3686_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114417","volume":"169","author":"X Yuan","year":"2021","unstructured":"Yuan, X., Shi, J., Gu, L.: A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst. Appl. 169, 114417 (2021)","journal-title":"Expert Syst. Appl."},{"key":"3686_CR33","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.mri.2020.09.018","volume":"76","author":"M Arshad","year":"2021","unstructured":"Arshad, M., Qureshi, M., Inam, O., Omer, H.: Transfer learning in deep neural network based under-sampled mr image reconstruction. Magn. Reson. Imaging 76, 96\u2013107 (2021)","journal-title":"Magn. Reson. Imaging"},{"key":"3686_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106258","volume":"204","author":"S Wang","year":"2020","unstructured":"Wang, S., Zhang, L., Fu, J.: Adversarial transfer learning for cross-domain visual recognition. Knowl.-Based Syst. 204, 106258 (2020)","journal-title":"Knowl.-Based Syst."},{"issue":"22","key":"3686_CR35","doi-asserted-by":"publisher","first-page":"29705","DOI":"10.1007\/s11042-018-6463-x","volume":"77","author":"K-H Thung","year":"2018","unstructured":"Thung, K.-H., Wee, C.-Y.: A brief review on multi-task learning. Multimedia Tools Appl. 77(22), 29705\u201329725 (2018)","journal-title":"Multimedia Tools Appl."},{"key":"3686_CR36","doi-asserted-by":"publisher","unstructured":"Kuang, B., Nnabuife, S.G., Whidborne, J.F., Sun, S., Zhao, J., Jenkins, K.: Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an s-shape riser. Expert Syst. Appl. 236, 121414 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121414.","DOI":"10.1016\/j.eswa.2023.121414."},{"key":"3686_CR37","doi-asserted-by":"crossref","unstructured":"Boschini, M., Bonicelli, L., Porrello, A., Bellitto, G., Pennisi, M., Palazzo, S., Spampinato, C., Calderara, S.: Transfer without forgetting. In: European Conference on Computer Vision, pp. 692\u2013709. Springer (2022)","DOI":"10.1007\/978-3-031-20050-2_40"},{"issue":"2","key":"3686_CR38","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1007\/s10489-020-01907-w","volume":"51","author":"M Liu","year":"2020","unstructured":"Liu, M., Yan, X., Wang, C., Wang, K.: Segmentation mask-guided person image generation. Appl. Intell. 51(2), 1161\u20131176 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01907-w","journal-title":"Appl. Intell."},{"key":"3686_CR39","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv:1306.5151 (2013)"},{"key":"3686_CR40","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wu, X., Chu, A., Huang, L., Wei, Z.: Swinfg: A fine-grained recognition scheme based on swin transformer. Expert Syst. Appl. 123021 (2023)","DOI":"10.1016\/j.eswa.2023.123021"},{"issue":"12","key":"3686_CR41","doi-asserted-by":"publisher","first-page":"9521","DOI":"10.1109\/TPAMI.2021.3126668","volume":"44","author":"R Du","year":"2021","unstructured":"Du, R., Xie, J., Ma, Z., Chang, D., Song, Y.-Z., Guo, J.: Progressive learning of category-consistent multi-granularity features for fine-grained visual classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 9521\u20139535 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"3686_CR42","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3390\/aerospace8050132","volume":"8","author":"F Nicolosi","year":"2021","unstructured":"Nicolosi, F., Corcione, S., Trifari, V., De Marco, A.: Design and optimization of a large turboprop aircraft. Aerospace 8(5), 132 (2021)","journal-title":"Aerospace"},{"key":"3686_CR43","doi-asserted-by":"crossref","unstructured":"Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., Han, J.: Towards large-scale small object detection: survey and benchmarks. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3290594"},{"key":"3686_CR44","doi-asserted-by":"crossref","unstructured":"Shao, Z., Yin, Y., Lyu, H., Soares, C.G., Cheng, T., Jing, Q., Yang, Z.: An efficient model for small object detection in the maritime environment. Appl. Ocean Res. 152, 104194 (2024)","DOI":"10.1016\/j.apor.2024.104194"},{"key":"3686_CR45","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.jmsy.2024.07.010","volume":"76","author":"G Kim","year":"2024","unstructured":"Kim, G., Yang, S.M., Kim, D.M., Choi, J.G., Lim, S., Park, H.W.: Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of ti-6al-4v. J. Manuf. Syst. 76, 133\u2013157 (2024)","journal-title":"J. Manuf. Syst."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03686-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03686-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03686-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T10:02:12Z","timestamp":1745488932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03686-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"references-count":45,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["3686"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03686-8","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,5]]},"assertion":[{"value":"9 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that they have no financial interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial interests"}},{"value":"All data have been used with ethical and informed consent.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}