{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:11:41Z","timestamp":1774570301018,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Karlsruher Institut f\u00fcr Technologie (KIT)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for utilizing both approaches. We introduce a synthetic data generator, which enables us to evaluate the impact of the number of training samples as well as the difficulty and diversity of the dataset. We show that deep learning methods excel when large datasets are available and conventional image processing approaches perform well when the datasets are small and diverse. Since transfer learning is a common approach to work around small datasets, we are specifically assessing its impact and found only marginal impact. Furthermore, we implement the conventional image processing pipeline to enable fast and easy application to new problems, making it easy to apply and test conventional methods alongside deep learning with minimal overhead.\n<\/jats:p>","DOI":"10.1007\/s00138-023-01501-3","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T13:02:41Z","timestamp":1706706161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A review of adaptable conventional image processing pipelines and deep learning on limited datasets"],"prefix":"10.1007","volume":"35","author":[{"given":"Friedrich Rieken","family":"M\u00fcnke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Sch\u00fctzke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Berens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Reischl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"1501_CR1","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR (2015). arXiv:1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1501_CR2","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. CoRR (2016). arXiv:1612.01105","DOI":"10.1109\/CVPR.2017.660"},{"key":"1501_CR3","unstructured":"Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. CoRR (2017). arXiv:1706.05587"},{"key":"1501_CR4","unstructured":"Yan, H., Zhang, C., Wu, M.: Lawin transformer: improving semantic segmentation transformer with multi-scale representations via large window attention. CoRR (2022). arXiv:2201.01615"},{"key":"1501_CR5","doi-asserted-by":"publisher","first-page":"265","DOI":"10.5772\/6080","volume":"8","author":"V Martin","year":"2008","unstructured":"Martin, V., Thonnat, M.: A cognitive vision approach to image segmentation. Tools Artif. Intell. 8, 265\u2013294 (2008). https:\/\/doi.org\/10.5772\/6080","journal-title":"Tools Artif. Intell."},{"issue":"32","key":"1501_CR6","first-page":"521","volume":"2019","author":"LFR Taveira","year":"2018","unstructured":"Taveira, L.F.R., Kurc, T., Melo, A.C.M.A., Kong, J., Bremer, E., Saltz, J.H., Teodoro, G.: Multi-objective parameter auto-tuning for tissue image segmentation workflows. J. Digit. Imaging 2019(32), 521\u2013533 (2018)","journal-title":"J. Digit. Imaging"},{"issue":"7","key":"1501_CR7","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1093\/bioinformatics\/btw749","volume":"33","author":"G Teodoro","year":"2017","unstructured":"Teodoro, G., Kur\u00e7, T.M., Taveira, L.F.R., Melo, A.C.M.A., Gao, Y., Kong, J., Saltz, J.H.: Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines. Bioinformatics 33(7), 1064\u20131072 (2017). https:\/\/doi.org\/10.1093\/bioinformatics\/btw749","journal-title":"Bioinformatics"},{"issue":"10","key":"1501_CR8","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/gb-2006-7-10-r100","volume":"7","author":"AE Carpenter","year":"2006","unstructured":"Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., Golland, P., Sabatini, D.M.: Cell profiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), 100 (2006). https:\/\/doi.org\/10.1186\/gb-2006-7-10-r100","journal-title":"Genome Biol."},{"key":"1501_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2021","unstructured":"Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23, 18 (2021). https:\/\/doi.org\/10.3390\/e23010018","journal-title":"Entropy"},{"key":"1501_CR10","doi-asserted-by":"publisher","unstructured":"Lin, D., Li, Y., Prasad, S., Nwe, T.L., Dong, S., Oo, Z.M.: CAM-UNET: class activation MAP guided UNET with feedback refinement for defect segmentation. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2131\u20132135 (2020). https:\/\/doi.org\/10.1109\/ICIP40778.2020.9190900","DOI":"10.1109\/ICIP40778.2020.9190900"},{"key":"1501_CR11","doi-asserted-by":"crossref","unstructured":"Mahony, N.O., Campbell, S., Carvalho, A., Harapanahalli, S., Velasco-Hern\u00e1ndez, G.A., Krpalkova, L., Riordan, D., Walsh, J.: Deep learning versus traditional computer vision. CoRR (2019). arXiv:1910.13796","DOI":"10.1007\/978-3-030-17795-9_10"},{"issue":"1","key":"1501_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 53 (2021). https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J. Big Data"},{"issue":"3","key":"1501_CR13","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.3233\/JIFS-169911","volume":"36","author":"S Anubha Pearline","year":"2019","unstructured":"Anubha Pearline, S., Sathiesh Kumar, V., Harini, S.: A study on plant recognition using conventional image processing and deep learning approaches. J. Intell. Fuzzy Syst. 36(3), 1997\u20132004 (2019). https:\/\/doi.org\/10.3233\/JIFS-169911","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"2","key":"1501_CR14","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.bbe.2019.01.005","volume":"39","author":"RB Hegde","year":"2019","unstructured":"Hegde, R.B., Prasad, K., Hebbar, H., Singh, B.M.K.: Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybern. Biomed. Eng. 39(2), 382\u2013392 (2019). https:\/\/doi.org\/10.1016\/j.bbe.2019.01.005","journal-title":"Biocybern. Biomed. Eng."},{"issue":"3","key":"1501_CR15","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/s10278-019-00307-y","volume":"33","author":"S Sharma","year":"2020","unstructured":"Sharma, S., Mehra, R.: Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images-a comparative insight. J. Digit. Imaging 33(3), 632\u2013654 (2020). https:\/\/doi.org\/10.1007\/s10278-019-00307-y","journal-title":"J. Digit. Imaging"},{"key":"1501_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/app9183935","volume":"9","author":"K Okayasu","year":"2019","unstructured":"Okayasu, K., Yoshida, K., Fuchida, M., Nakamura, A.: Vision-based classification of mosquito species: comparison of conventional and deep learning methods. Appl Sci 9, 18 (2019). https:\/\/doi.org\/10.3390\/app9183935","journal-title":"Appl Sci"},{"key":"1501_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3390\/diagnostics11030528","volume":"11","author":"S Boumaraf","year":"2021","unstructured":"Boumaraf, S., Liu, X., Wan, Y., Zheng, Z., Ferkous, C., Ma, X., Li, Z., Bardou, D.: Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: a comparative study with visual explanation. Diagnostics 11, 3 (2021). https:\/\/doi.org\/10.3390\/diagnostics11030528","journal-title":"Diagnostics"},{"key":"1501_CR18","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang, P., Fan, E., Wang, P.: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn. Lett. 141, 61\u201367 (2021)","journal-title":"Pattern Recogn. Lett."},{"issue":"11","key":"1501_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1005177","volume":"12","author":"DA Van Valen","year":"2016","unstructured":"Van Valen, D.A., Kudo, T., Lane, K.M., Macklin, D.N., Quach, N.T., DeFelice, M.M., Maayan, I., Tanouchi, Y., Ashley, E.A., Covert, M.W.: Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12(11), 1\u201324 (2016). https:\/\/doi.org\/10.1371\/journal.pcbi.1005177","journal-title":"PLoS Comput. Biol."},{"key":"1501_CR20","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/s20113085","volume":"20","author":"R Brehar","year":"2020","unstructured":"Brehar, R., Mitrea, D.-A., Vancea, F., Marita, T., Nedevschi, S., Lupsor-Platon, M., Rotaru, M., Badea, R.I.: Comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images. Sensors 20, 11 (2020). https:\/\/doi.org\/10.3390\/s20113085","journal-title":"Sensors"},{"key":"1501_CR21","doi-asserted-by":"publisher","unstructured":"Harangi, B., Toth, J., Bogacsovics, G., Kupas, D., Kovacs, L., Hajdu, A.: Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 38\u201342 (2019). https:\/\/doi.org\/10.1109\/ISPA.2019.8868683","DOI":"10.1109\/ISPA.2019.8868683"},{"key":"1501_CR22","doi-asserted-by":"publisher","unstructured":"Fotin, S.V., Yin, Y., Haldankar, H., Hoffmeister\u00a0M.D., J.W., Periaswamy, S.: Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In: Tourassi, G.D. (eds.) Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, pp. 228\u2013233. International Society for Optics and Photonics (2016). https:\/\/doi.org\/10.1117\/12.2217045","DOI":"10.1117\/12.2217045"},{"issue":"34249654","key":"1501_CR23","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.21037\/qims-20-1356","volume":"11","author":"F Bianconi","year":"2021","unstructured":"Bianconi, F., Fravolini, M.L., Pizzoli, S., Palumbo, I., Minestrini, M., Rondini, M., Nuvoli, S., Spanu, A., Palumbo, B.: Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant. Imaging Med. Surg. 11(34249654), 3286\u20133305 (2021). https:\/\/doi.org\/10.21037\/qims-20-1356","journal-title":"Quant. Imaging Med. Surg."},{"key":"1501_CR24","doi-asserted-by":"publisher","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","volume":"234","author":"Y Ren","year":"2020","unstructured":"Ren, Y., Huang, J., Hong, Z., Lu, W., Yin, J., Zou, L., Shen, X.: Image-based concrete crack detection in tunnels using deep fully convolutional networks. Construct. Build. Mater. 234, 117367 (2020). https:\/\/doi.org\/10.1016\/j.conbuildmat.2019.117367","journal-title":"Construct. Build. Mater."},{"issue":"10","key":"1501_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0230605","volume":"15","author":"C Karaba\u011f","year":"2020","unstructured":"Karaba\u011f, C., Jones, M.L., Peddie, C.J., Weston, A.E., Collinson, L.M., Reyes-Aldasoro, C.C.: Semantic segmentation of HeLa cells: an objective comparison between one traditional algorithm and four deep-learning architectures. PLoS ONE 15(10), 1\u201321 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0230605","journal-title":"PLoS ONE"},{"key":"1501_CR26","doi-asserted-by":"crossref","unstructured":"King, A., Bhandarkar, S.M., Hopkinson, B.M.: A comparison of deep learning methods for semantic segmentation of coral reef survey images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00188"},{"key":"1501_CR27","unstructured":"Ofir, N., Nebel, J.: Classic versus deep approaches to address computer vision challenges. CoRR (2021). arXiv:2101.09744"},{"issue":"1","key":"1501_CR28","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979). https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"9","key":"1501_CR29","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/cyto.a.23863","volume":"95","author":"JC Caicedo","year":"2019","unstructured":"Caicedo, J.C., Roth, J., Goodman, A., Becker, T., Karhohs, K.W., Broisin, M., Molnar, C., McQuin, C., Singh, S., Theis, F.J., Carpenter, A.E.: Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. Cytometry A 95(9), 952\u2013965 (2019). https:\/\/doi.org\/10.1002\/cyto.a.23863","journal-title":"Cytometry A"},{"issue":"12","key":"1501_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0243219","volume":"15","author":"T Scherr","year":"2020","unstructured":"Scherr, T., L\u00f6ffler, K., B\u00f6hland, M., Mikut, R.: Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy. PLoS ONE 15(12), 1\u201322 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0243219","journal-title":"PLoS ONE"},{"key":"1501_CR31","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/jimaging7040071","volume":"7","author":"J Le\u2019Clerc Arrastia","year":"2021","unstructured":"Le\u2019Clerc Arrastia, J., Heilenk\u00f6tter, N., Otero Baguer, D., Hauberg-Lotte, L., Boskamp, T., Hetzer, S., Duschner, N., Schaller, J., Maass, P.: Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of basal cell carcinoma. J. Imaging 7, 4 (2021). https:\/\/doi.org\/10.3390\/jimaging7040071","journal-title":"J. Imaging"},{"key":"1501_CR32","doi-asserted-by":"publisher","first-page":"2753","DOI":"10.1109\/ACCESS.2022.3140378","volume":"10","author":"M Schilling","year":"2022","unstructured":"Schilling, M., Scherr, T., M\u00fcnke, F.R., Neumann, O., Schutera, M., Mikut, R., Reischl, M.: Automated annotator variability inspection for biomedical image segmentation. IEEE Access 10, 2753\u20132765 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3140378","journal-title":"IEEE Access"},{"key":"1501_CR33","volume-title":"Segmentation Models","author":"P Iakubovskii","year":"2019","unstructured":"Iakubovskii, P.: Segmentation Models. GitHub (2019)"},{"key":"1501_CR34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1501_CR35","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Fei-Fei, L.: ImageNet large scale visual recognition challenge. In: IJCV (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"1501_CR36","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41592-021-01249-6","volume":"18","author":"C Edlund","year":"2021","unstructured":"Edlund, C., Jackson, T.R., Khalid, N., Bevan, N., Dale, T., Dengel, A., Ahmed, S., Trygg, J., Sj\u00f6gren, R.: LIVECell\u2014a large-scale dataset for label-free live cell segmentation. Nat. Methods 18, 9 (2021). https:\/\/doi.org\/10.1038\/s41592-021-01249-6","journal-title":"Nat. Methods"},{"key":"1501_CR37","unstructured":"Pugliatti, M., Topputo, F.: DOORS: Dataset for Boulders Segmentation. Statistical Properties and Blender Setup (2022)"},{"key":"1501_CR38","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., Halpern, A.: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC) (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"1501_CR39","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Schaefer, G., Bancher, B., L\u00f6w, C., Dorffner, G., Ecker, R., Ellinger, I.: CryoNuSeg: a dataset for nuclei instance segmentation of cryosectioned H &E-stained histological images. Comput. Biol. Med. 132(104349), x (2021)","DOI":"10.1016\/j.compbiomed.2021.104349"},{"key":"1501_CR40","doi-asserted-by":"publisher","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE (2017). https:\/\/doi.org\/10.1109\/vcip.2017.8305148","DOI":"10.1109\/vcip.2017.8305148"},{"key":"1501_CR41","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.106"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01501-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01501-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01501-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T06:37:00Z","timestamp":1711175820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01501-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,31]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1501"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01501-3","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,31]]},"assertion":[{"value":"3 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2024","order":4,"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 financial, non-financial or other competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The code is made fully available.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability:"}}],"article-number":"25"}}