{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T02:11:16Z","timestamp":1772244676254,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NTNU Norwegian University of Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-based breast lesion segmentation (BLS) in breast DCE-MRI.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The study utilized the Stavanger Dataset, comprising 59 DCE-MRI scans, and employed the UNet++ architecture as the segmentation model. Four experimental approaches were designed to assess the influence of BRS on BLS: (1) Whole Volume (WV) without BRS, (2) WV with BRS, (3) BRS applied only to Selected Lesion-containing Slices (SLS), and (4) BRS applied to an Optimal Volume (OV). Data augmentation and oversampling techniques were implemented to address dataset limitations and enhance model generalizability. A systematic method was employed to determine OV sizes for patient\u2019s DCE-MRI images ensuring full lesion inclusion. Model training and validation were conducted using a hybrid loss function\u2014comprising Dice loss, focal loss, and cross-entropy loss\u2014and a five-fold cross-validation strategy. Final evaluations were performed on a randomly split test dataset for each of the four approaches.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The findings indicate that applying BRS significantly enhances model performance. The most notable improvement was observed in the fourth approach, BRS with OV, which achieved approximately a 50% increase in segmentation accuracy compared to the non-BRS baseline. Furthermore, the BRS with OV approach resulted in a substantial reduction in computational energy consumption\u2014up to 450%, highlighting its potential as an environmentally sustainable solution for large-scale applications.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12880-025-01947-z","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T08:30:00Z","timestamp":1760085000000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance"],"prefix":"10.1186","volume":"25","author":[{"given":"Sam","family":"Narimani","sequence":"first","affiliation":[]},{"given":"Solveig Roth","family":"Hoff","sequence":"additional","affiliation":[]},{"given":"Kathinka D\u00e6hli","family":"Kurz","sequence":"additional","affiliation":[]},{"given":"Kjell-Inge","family":"Gjesdal","sequence":"additional","affiliation":[]},{"given":"J\u00fcrgen","family":"Geisler","sequence":"additional","affiliation":[]},{"given":"Endre","family":"Gr\u00f8vik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"issue":"7261","key":"1947_CR1","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1136\/bmj.321.7261.624","volume":"321","author":"K McPherson","year":"2000","unstructured":"McPherson K, Steel C, Dixon J. Breast cancer\u2014epidemiology, risk factors, and genetics. BMJ. 2000;321(7261):624\u201328.","journal-title":"BMJ"},{"key":"1947_CR2","unstructured":"Cancer registry of Norway: cancer in Norway 2023 - cancer incidence, mortality, survival and prevalence in Norway. Cancer Registry of Norway, Oslo (2024). Norwegian Institute of Public Health."},{"key":"1947_CR3","unstructured":"Norwegian Cancer Registry: Breast Cancer; 2024. Last updated: 07.05.2024. Accessed: 2025-01-21. https:\/\/www.kreftregisteret.no\/kreftformer\/Brystkreft\/"},{"issue":"3","key":"1947_CR4","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1148\/radiol.2019182947","volume":"292","author":"RM Mann","year":"2019","unstructured":"Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology. 2019;292(3):520\u201336.","journal-title":"Radiology"},{"issue":"3","key":"1947_CR5","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1002\/(SICI)1522-2586(199909)10:3<260::AID-JMRI6>3.0.CO;2-7","volume":"10","author":"M Knopp","year":"1999","unstructured":"Knopp M, Weiss E, Sinn H, Mattern J, Junkermann H, Radeleff J, Magener A, Brix G, Delorme S, Zuna I, et al. Pathophysiologic basis of contrast enhancement in breast tumors. J Educ Chang Magnetic Reson Imag: An Off J Int Soc For Magnetic Reson In Med. 1999;10(3):260\u201366.","journal-title":"J Educ Chang Magnetic Reson Imag: An Off J Int Soc For Magnetic Reson In Med"},{"key":"1947_CR6","unstructured":"Organization WH. Guide to cancer early diagnosis. World Health Organ. 2017. Accessed:2025\u201301\u201323. https:\/\/www.who.int\/publications\/i\/item\/guide-to-cancer-early-diagnosis"},{"key":"1947_CR7","unstructured":"Alshawwa IA, El-Mashharawi HQ, Salman FM, Al-Qumboz MNA, Abunasser BS. Abu-naser SS. Advancements in early detection of breast cancer: innovations and future directions. 2024."},{"key":"1947_CR8","doi-asserted-by":"crossref","first-page":"984626","DOI":"10.3389\/fonc.2022.984626","volume":"12","author":"W Yue","year":"2022","unstructured":"Yue W, Zhang H, Zhou J, Li G, Tang Z, Sun Z, Cai J, Tian N, Gao S, Dong J, et al. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging. Front Oncol. 2022;12:984626.","journal-title":"Front Oncol"},{"key":"1947_CR9","doi-asserted-by":"crossref","unstructured":"Benjelloun M, El Adoui M, Larhmam MA, Mahmoudi SA. Automated breast tumor segmentation in DCE-MRI using deep learning. In: 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech). IEEE; 2018. pp. 1\u20136.","DOI":"10.1109\/CloudTech.2018.8713352"},{"key":"1947_CR10","first-page":"97","volume-title":"Medical imaging 2019: Imaging informatics for healthcare, research, and applications","author":"L Zhang","year":"2019","unstructured":"Zhang L, Luo Z, Chai R, Arefan D, Sumkin J, Wu S. Deep-learning method for tumor segmentation in breast DCE-MRI. In: Medical imaging 2019: Imaging informatics for healthcare, research, and applications, Vol. 10954. SPIE; 2019. pp. 97\u2013102."},{"key":"1947_CR11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.acra.2020.12.001","volume":"29","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Chan S, Park VY, Chang K-T, Mehta S, Kim MJ, Combs, F.J., Chang P, Chow D, Parajuli R, et al. Automatic detection and segmentation of breast cancer on MRI using mask R-CNN trained on non\u2013fat-sat images and tested on fat-sat images. Academic Radiol. 2022;29:135\u201344.","journal-title":"Academic Radiol"},{"key":"1947_CR12","doi-asserted-by":"crossref","first-page":"102925","DOI":"10.1016\/j.bspc.2021.102925","volume":"69","author":"DK Patra","year":"2021","unstructured":"Patra DK, Si T, Mondal S, Mukherjee P. Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization. Biomed Signal Process And Control. 2021;69:102925.","journal-title":"Biomed Signal Process And Control"},{"key":"1947_CR13","doi-asserted-by":"crossref","unstructured":"Dhungel N, Carneiro G, Bradley AP. Deep learning and structured prediction for the segmentation of mass in mammograms. International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. pp. 605\u201312.","DOI":"10.1007\/978-3-319-24553-9_74"},{"issue":"4","key":"1947_CR14","first-page":"35","volume":"10","author":"BC Patel","year":"2010","unstructured":"Patel BC, Sinha G. An adaptive K-means clustering algorithm for breast image segmentation. Int J Comput Appl. 2010;10(4):35\u201338.","journal-title":"Int J Comput Appl"},{"issue":"6","key":"1947_CR15","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1016\/j.patcog.2008.08.006","volume":"42","author":"AR Dom\u00ednguez","year":"2009","unstructured":"Dom\u00ednguez AR, Nandi AK. Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern recognition. 2009;42(6):1138\u201348.","journal-title":"Pattern recognition"},{"issue":"2","key":"1947_CR16","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1109\/TMI.2018.2865671","volume":"38","author":"J Zhang","year":"2018","unstructured":"Zhang J, Saha A, Zhu Z, Mazurowski MA. Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics. IEEE Trans Med ImAging. 2018;38(2):435\u201347.","journal-title":"IEEE Trans Med ImAging"},{"issue":"1","key":"1947_CR17","first-page":"2413706","volume":"2020","author":"H Jiao","year":"2020","unstructured":"Jiao H, Jiang X, Pang Z, Lin X, Huang Y, Li L. Deep convolutional neural networks-based automatic breast segmentation and mass detection in DCE-MRI. Comput Math Method M. 2020;2020(1):2413706.","journal-title":"Comput Math Method M"},{"key":"1947_CR18","doi-asserted-by":"crossref","first-page":"119962","DOI":"10.1016\/j.eswa.2023.119962","volume":"224","author":"R Huang","year":"2023","unstructured":"Huang R, Xu Z, Xie Y, Wu H, Li Z, Cui Y, Huo Y, Han C, Yang X, Liu Z, et al. Joint-phase attention network for breast cancer segmentation in DCE-MRI. Expert Syst With Appl. 2023;224:119962.","journal-title":"Expert Syst With Appl"},{"key":"1947_CR19","doi-asserted-by":"crossref","first-page":"106691","DOI":"10.1016\/j.bspc.2024.106691","volume":"99","author":"CSPS Star","year":"2025","unstructured":"Star CSPS, Inbamalar T, Milton A. Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50. Biomed Signal Process and Control. 2025;99:106691.","journal-title":"Biomed Signal Process And Control"},{"issue":"17","key":"1947_CR20","doi-asserted-by":"crossref","first-page":"8317","DOI":"10.1007\/s00500-022-07235-0","volume":"26","author":"C Qin","year":"2022","unstructured":"Qin C, Wu Y, Zeng J, Tian L, Zhai Y, Li F, Zhang X. Joint transformer and multi-scale CNN for DCE-MRI breast cancer segmentation. Soft Comput. 2022;26(17):8317\u201334.","journal-title":"Soft Comput"},{"key":"1947_CR21","doi-asserted-by":"crossref","first-page":"105093","DOI":"10.1016\/j.compbiomed.2021.105093","volume":"140","author":"R Khaled","year":"2022","unstructured":"Khaled R, Vidal J, Vilanova JC, Marti R. A U-Net ensemble for breast lesion segmentation in DCE MRI. Comput Biol Med. 2022;140:105093.","journal-title":"Comput Biol Med"},{"key":"1947_CR22","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\u2013MICCAI 2015: 18th international conference. Munich, Germany: Springer; 2015, pp. 234\u201341, October 5\u20139, 2015, proceedings, Part III 18.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"1947_CR23","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1038\/s41523-021-00358-x","volume":"7","author":"A Baccouche","year":"2021","unstructured":"Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby AS. Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer. 2021;7(1):151.","journal-title":"NPJ Breast Cancer"},{"key":"1947_CR24","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop,ML-CDS 2018, Held in Conjunction with MICCAI 2018. Granada, Spain: Springer; 2018, pp. 3\u201311, September 20, 2018, Proceedings 4.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1947_CR25","doi-asserted-by":"crossref","first-page":"105093","DOI":"10.1016\/j.compbiomed.2021.105093","volume":"140","author":"J Vidal","year":"2022","unstructured":"Vidal J, Vilanova JC, Mart\u00ed R, et al. A U-Net ensemble for breast lesion segmentation in DCE MRI. Comput Biol Med. 2022;140:105093.","journal-title":"Comput Biol Med"},{"issue":"1","key":"1947_CR26","first-page":"3470764","volume":"2022","author":"C Qin","year":"2022","unstructured":"Qin C, Lin J, Zeng J, Zhai Y, Tian L, Peng S, Li F. Joint dense residual and recurrent attention network for DCE-MRI breast tumor segmentation. Comput Intel neurosci. 2022;2022(1):3470764.","journal-title":"Comput Intel neurosci"},{"issue":"1","key":"1947_CR27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s13755-022-00176-w","volume":"10","author":"D Pandey","year":"2022","unstructured":"Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf Sci Syst. 2022;10(1):9.","journal-title":"Health Inf Sci Syst"},{"key":"1947_CR28","doi-asserted-by":"crossref","first-page":"110393","DOI":"10.1016\/j.knosys.2023.110393","volume":"267","author":"A Iqbal","year":"2023","unstructured":"Iqbal A, Sharif M. BTS-ST: swin transformer network for segmentation and classification of multimodality breast cancer images. Knowl-Based Syst. 2023;267:110393.","journal-title":"Knowl-Based Syst"},{"issue":"13","key":"1947_CR29","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.1049\/ipr2.12897","volume":"17","author":"J Zhang","year":"2023","unstructured":"Zhang J, Zhang Z, Liu H, Xu S. SaTransformer: Semantic-aware transformer for breast cancer classification and segmentation. IET Image process. 2023;17(13):3789\u2013800.","journal-title":"IET Image process"},{"key":"1947_CR30","doi-asserted-by":"crossref","unstructured":"Zhou L, Zhang Y, Zhang J, Qian X, Gong C, Sun K, Ding Z, Wang X, Li Z, Liu Z, et al. Prototype learning guided hybrid network for breast tumor segmentation in dce-mri. IEEE Transactions on Medical Imaging. 2024.","DOI":"10.1109\/TMI.2024.3435450"},{"key":"1947_CR31","doi-asserted-by":"crossref","unstructured":"Babu P, Asaithambi M, Suriyakumar SM. Contextual regularization-based energy optimization for segmenting breast tumor in DCE-MRI. IEEE Access. 2025.","DOI":"10.1109\/ACCESS.2025.3553035"},{"key":"1947_CR32","doi-asserted-by":"crossref","first-page":"107656","DOI":"10.1016\/j.bspc.2025.107656","volume":"105","author":"Y Zhong","year":"2025","unstructured":"Zhong Y, Xu Z, Han C, Liu Z, Wang Y. Bounding boxes for weakly-supervised breast cancer segmentation in DCE-MRI. Biomed Signal Process and Control. 2025;105:107656.","journal-title":"Biomed Signal Process And Control"},{"issue":"3","key":"1947_CR33","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.3390\/app15031295","volume":"15","author":"H Wang","year":"2025","unstructured":"Wang H, Wei L, Liu B, Li J, Li J, Fang J, Mooney C. Transformer-based explainable Model for breast cancer lesion segmentation. Appl Sci. 2025;15(3):1295.","journal-title":"Appl Sci"},{"key":"1947_CR34","doi-asserted-by":"crossref","unstructured":"Narimani S, Hoff SR, Kurz KD, Gjesdal K-I, Geisler J, Grovik E. Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal. arXiv preprint arXiv:2410.02337 2024.","DOI":"10.1038\/s41598-025-92863-3"},{"key":"1947_CR35","doi-asserted-by":"crossref","unstructured":"Hesaraki S, Akbari M, Mousa R. Unet++ and LSTM combined approach for breast ultrasound image segmentation. arXiv preprint arXiv:2412.05585 2024.","DOI":"10.2139\/ssrn.5033318"},{"issue":"19","key":"1947_CR36","doi-asserted-by":"crossref","first-page":"57449","DOI":"10.1007\/s11042-023-17768-7","volume":"83","author":"A Kanadath","year":"2024","unstructured":"Kanadath A, Jothi JAA, Urolagin S. AIR-UNet++: a deep learning framework for histopathology image segmentation and detection. Multimedia Tools Appl. 2024;83(19):57449\u201375.","journal-title":"Multimedia Tools Appl"},{"key":"1947_CR37","doi-asserted-by":"crossref","unstructured":"Robin M, John J, Ravikumar A. Breast tumor segmentation using U-net. In: 2021 5th international conference on computing methodologies and communication (ICCMC). IEEE; 2021. pp. 1164\u201367.","DOI":"10.1109\/ICCMC51019.2021.9418447"},{"key":"1947_CR38","unstructured":"Jalalian K, Hosseini G, Ghiasi R, Bosaghzadeh A. Improving detection of breast cancer with Unet++ deep learning framework in digital mammography. Front Biomed Technol. 2025;12."},{"key":"1947_CR39","doi-asserted-by":"crossref","first-page":"106501","DOI":"10.1016\/j.compbiomed.2022.106501","volume":"158","author":"J Li","year":"2023","unstructured":"Li J, Liu K, Hu Y, Zhang H, Heidari AA, Chen H, Zhang W, Algarni AD, Elmannai H. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and res-UNet++. Comput Biol Med. 2023;158:106501.","journal-title":"Comput Biol Med"},{"key":"1947_CR40","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi. S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). Vision (3DV). IEEE; 2016. pp. 565\u201371.","DOI":"10.1109\/3DV.2016.79"},{"key":"1947_CR41","doi-asserted-by":"crossref","unstructured":"Lin T. Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 2017.","DOI":"10.1109\/ICCV.2017.324"},{"key":"1947_CR42","unstructured":"Mao A, Mohri M, Zhong Y. Cross-entropy loss functions: theoretical analysis and applications. In: International conference on machine learning. PMLR; 2023. pp. 23803\u201328."},{"key":"1947_CR43","doi-asserted-by":"crossref","unstructured":"Strubell E, Ganesh A, McCallum A. Energy and policy considerations for modern deep learning research. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020, pp. 13693\u201396, vol. 34.","DOI":"10.1609\/aaai.v34i09.7123"},{"key":"1947_CR44","unstructured":"IEA: global energy & CO2 status report 2019; 2019. Licence: CC BY 4.0. 2019. https:\/\/www.iea.org\/reports\/global-energy-co2-status-report-2019"},{"issue":"25","key":"1947_CR45","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1080\/00036846.2023.2203461","volume":"56","author":"J Zhong","year":"2024","unstructured":"Zhong J, Zhong Y, Han M, Yang T, Zhang Q. The impact of AI on carbon emissions: evidence from 66 countries. Appl Econ. 2024;56(25):2975\u201389.","journal-title":"Appl Econ Lett"},{"issue":"1","key":"1947_CR46","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/philosophies7010004","volume":"7","author":"G Tamburrini","year":"2022","unstructured":"Tamburrini G. The AI carbon footprint and responsibilities of AI scientists. Philosophies. 2022;7(1):4.","journal-title":"Philosophies"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01947-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01947-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01947-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T12:24:41Z","timestamp":1760358281000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01947-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1947"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01947-z","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"7 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the principles of the Declaration of Helsinki. The data were obtained from a project previously approved by the Regional Committee for Medical and Health Research Ethics, Western Norway (REK West), in connection with its implementation at Stavanger University Hospital. Written informed consent was obtained from all participants prior to inclusion.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"406"}}