{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:23:50Z","timestamp":1780392230973,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008905","name":"University of Klagenfurt","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008905","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized physicians and challenging to classify or localize via computer-aided analysis. In this work, we take a first step in the direction of localized endometriosis recognition in laparoscopic gynecology videos using region-based deep neural networks Faster R-CNN and Mask R-CNN. We in particular use and further develop publicly available data for transfer learning deep detection models according to distinctive visual lesion characteristics. Subsequently, we evaluate the performance impact of different data augmentation techniques, including selected geometrical and visual transformations, specular reflection removal as well as region tracking across video frames. Finally, particular attention is given to creating reasonable data segmentation for training, validation and testing. The best performing result surprisingly is achieved by randomly applying simple cropping combined with rotation, resulting in a mean average segmentation precision of 32.4% at 50-95% intersection over union overlap (64.2% for 50% overlap).<\/jats:p>","DOI":"10.1007\/s11042-021-11730-1","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T18:03:02Z","timestamp":1641578582000},"page":"6191-6215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Endometriosis detection and localization in laparoscopic gynecology"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9535-966X","authenticated-orcid":false,"given":"Andreas","family":"Leibetseder","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Schoeffmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rg","family":"Keckstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Keckstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"issue":"5","key":"11730_CR1","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/S0015-0282(97)81391-X","volume":"67","author":"M Canis","year":"1997","unstructured":"Canis M, Donnez J, Guzick D, Halme J, Rock J, Schenken R, Vernon M (1997) Revised american society for reproductive medicine classification of endometriosis: 1996. Fertility and Sterility 67(5):817\u2013821. https:\/\/doi.org\/10.1016\/S0015-0282(97)81391-X","journal-title":"Fertility and Sterility"},{"key":"11730_CR2","doi-asserted-by":"publisher","first-page":"142053","DOI":"10.1109\/ACCESS.2019.2944676","volume":"7","author":"W Du","year":"2019","unstructured":"Du W, Rao N, Liu D, Jiang H, Luo C, Li Z, Gan T, Zeng B (2019) Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images. IEEE Access 7:142053\u2013142069","journal-title":"IEEE Access"},{"key":"11730_CR3","doi-asserted-by":"crossref","unstructured":"Fox M, Taschwer M, Schoeffmann K (2020) Pixel-based tool segmentation in cataract surgery videos with mask r-cnn. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS), IEEE, pp 565\u2013568","DOI":"10.1109\/CBMS49503.2020.00112"},{"key":"11730_CR4","doi-asserted-by":"crossref","unstructured":"Fu Y, Robu MR, Koo B, Schneider C, van Laarhoven S, Stoyanov D, Davidson B, Clarkson MJ, Hu Y (2019) More unlabelled data or label more data? a study on semi-supervised laparoscopic image segmentation. In: Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, Springer, pp 173\u2013180","DOI":"10.1007\/978-3-030-33391-1_20"},{"key":"11730_CR5","doi-asserted-by":"crossref","unstructured":"Gibson E, Robu MR, Thompson S, Edwards PE, Schneider C, Gurusamy K, Davidson B, Hawkes DJ, Barratt DC, Clarkson MJ (2017) Deep residual networks for automatic segmentation of laparoscopic videos of the liver. In: Medical imaging 2017: Image-guided procedures, robotic interventions, and modeling, international society for optics and photonics, vol 10135, pp 101351M","DOI":"10.1117\/12.2255975"},{"key":"11730_CR6","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"11730_CR7","unstructured":"Grammatikopoulou M, Flouty E, Kadkhodamohammadi A, Quellec G, Chow A, Nehme J, Luengo I, Stoyanov D (2019) Cadis: Cataract dataset for image segmentation. arXiv:190611586"},{"key":"11730_CR8","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"11730_CR9","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick RB (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 42(2):386\u2013397. https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"11730_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2014","unstructured":"Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3):583\u2013596","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"11730_CR11","doi-asserted-by":"crossref","unstructured":"Jha D, Ali S, Johansen HD, Johansen DD, Rittscher J, Riegler MA, Halvorsen P (2020) Real-time polyp detection, localisation and segmentation in colonoscopy using deep learning. arXiv:201107631","DOI":"10.1109\/ACCESS.2021.3063716"},{"key":"11730_CR12","doi-asserted-by":"crossref","unstructured":"Jin A, Yeung S, Jopling J, Krause J, Azagury D, Milstein A, Fei-Fei L (2018) Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 691\u2013699","DOI":"10.1109\/WACV.2018.00081"},{"key":"11730_CR13","unstructured":"Jung AB, Wada K, Crall J, Tanaka S, Graving J, Reinders C, Yadav S, Banerjee J, Vecsei G, Kraft A, Rui Z, Borovec J, Vallentin C, Zhydenko S, Pfeiffer K, Cook B, Fern\u00e1ndez I, De Rainville FM, Weng CH, Ayala-Acevedo A, Meudec R, Laporte M, et al. (2020) Imgaug. https:\/\/github.com\/aleju\/imgaug, Accessed 01 Feb 2020"},{"key":"11730_CR14","doi-asserted-by":"crossref","unstructured":"Keckstein J, Hudelist G (2020) Classification of die including bowel endometriosis: from r-asrm to #enzian-classification. Best Pract Res Clin Obstet Gynaecol","DOI":"10.1016\/j.bpobgyn.2020.11.004"},{"key":"11730_CR15","first-page":"291","volume":"125","author":"J Keckstein","year":"2003","unstructured":"Keckstein J, Ulrich U, Possover M, Schweppe K et al (2003) Enzian-klassifikation der tief infiltrierenden endometriose. Zentralblatt f\u00fcr Gyn\u00e4kologie 125:291","journal-title":"Zentralblatt f\u00fcr Gyn\u00e4kologie"},{"key":"11730_CR16","doi-asserted-by":"crossref","unstructured":"Kletz S, Schoeffmann K, Benois-Pineau J, Husslein H (2019) Identifying surgical instruments in laparoscopy using deep learning instance segmentation. In: 2019 International conference on content-based multimedia indexing (CBMI), IEEE, pp 1\u20136","DOI":"10.1109\/CBMI.2019.8877379"},{"key":"11730_CR17","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25, Curran Associates, Inc., pp 1097\u20131105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"11730_CR18","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1145\/3126686.3126690","volume-title":"Proceedings of the on thematic workshops of ACM multimedia 2017, October 23 - 27, 2017","author":"A Leibetseder","year":"2017","unstructured":"Leibetseder A, Primus MJ, Petscharnig S, Schoeffmann K (2017) Real-time image-based smoke detection in endoscopic videos. In: Wu W, Yang J, Tian Q, Zimmermann R (eds) Proceedings of the on thematic workshops of ACM multimedia 2017, October 23 - 27, 2017. ACM, Mountain View, CA, USA, pp 296\u2013304. https:\/\/doi.org\/10.1145\/3126686.3126690"},{"key":"11730_CR19","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1145\/3204949.3208127","volume-title":"Proceedings of the 9th ACM multimedia systems conference, MMSys 2018, June 12-15, 2018","author":"A Leibetseder","year":"2018","unstructured":"Leibetseder A, Petscharnig S, Primus MJ, Kletz S, M\u00fcnzer B, Schoeffmann K, Keckstein J (2018) Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology. In: C\u00e9sar P, Zink M, Murray N (eds) Proceedings of the 9th ACM multimedia systems conference, MMSys 2018, June 12-15, 2018. ACM, Amsterdam, The Netherlands, pp 357\u2013362. https:\/\/doi.org\/10.1145\/3204949.3208127"},{"key":"11730_CR20","doi-asserted-by":"publisher","unstructured":"Leibetseder A, Kletz S, Schoeffmann K, Keckstein S, Keckstein J (2020) GLENDA: gynecologic laparoscopy endometriosis dataset. In: Ro YM, Cheng W, Kim J, Chu W, Cui P, Choi J, Hu M, Neve WD (eds) MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5-8, 2020, Proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 11962, pp 439\u2013450. https:\/\/doi.org\/10.1007\/978-3-030-37734-2_36,","DOI":"10.1007\/978-3-030-37734-2_36"},{"key":"11730_CR21","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"11730_CR22","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Medical Image Analysis 42:60\u201388","journal-title":"Medical Image Analysis"},{"key":"11730_CR23","doi-asserted-by":"crossref","unstructured":"Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2020) Image segmentation using deep learning: A survey. arXiv:200105566","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"11730_CR24","doi-asserted-by":"publisher","unstructured":"M\u00fcnzer B, Schoeffmann K, B\u00f6sz\u00f6rmenyi L (2017) Content-based processing and analysis of endoscopic images and videos: A survey. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-016-4219-z","DOI":"10.1007\/s11042-016-4219-z"},{"key":"11730_CR25","doi-asserted-by":"publisher","unstructured":"M\u00fcnzer B, Leibetseder A, Kletz S, Schoeffmann K (2019) ECAT - endoscopic concept annotation tool. In: Kompatsiaris I, Huet B, Mezaris V, Gurrin C, Cheng W, Vrochidis S (eds) MultiMedia Modeling - 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8-11, 2019, Proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 11296, pp 571\u2013576. https:\/\/doi.org\/10.1007\/978-3-030-05716-9_48,","DOI":"10.1007\/978-3-030-05716-9_48"},{"issue":"6","key":"11730_CR26","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s11548-019-01958-6","volume":"14","author":"CI Nwoye","year":"2019","unstructured":"Nwoye CI, Mutter D, Marescaux J, Padoy N (2019) Weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos. InternationAl Journal of Computer Assisted Radiology and Surgery 14(6):1059\u20131067","journal-title":"InternationAl Journal of Computer Assisted Radiology and Surgery"},{"key":"11730_CR27","doi-asserted-by":"publisher","first-page":"175628482091065","DOI":"10.1177\/1756284820910659","volume":"13","author":"T Ozawa","year":"2020","unstructured":"Ozawa T, Ishihara S, Fujishiro M, Kumagai Y, Shichijo S, Tada T (2020) Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. TherapeutiC Advances in Gastroenterology 13:1756284820910659","journal-title":"TherapeutiC Advances in Gastroenterology"},{"key":"11730_CR28","doi-asserted-by":"publisher","unstructured":"Park SY, Sargent D (2016) Colonoscopic polyp detection using convolutional neural networks. In: Tourassi GD, Armato SG (eds) Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March 2016, International Society for Optics and Photonics, pp 978528. https:\/\/doi.org\/10.1117\/12.2217148","DOI":"10.1117\/12.2217148"},{"issue":"7","key":"11730_CR29","doi-asserted-by":"publisher","first-page":"8061","DOI":"10.1007\/s11042-017-4699-5","volume":"77","author":"S Petscharnig","year":"2018","unstructured":"Petscharnig S, Sch\u00f6ffmann K (2018) Learning laparoscopic video shot classification for gynecological surgery. Multimedia Tools and Applications 77(7):8061\u20138079","journal-title":"Multimedia Tools and Applications"},{"key":"11730_CR30","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.inffus.2020.09.006","volume":"66","author":"F Piccialli","year":"2021","unstructured":"Piccialli F, Di Somma V, Giampaolo F, Cuomo S, Fortino G (2021) A survey on deep learning in medicine: Why, how and when? Information Fusion 66:111\u2013137","journal-title":"Information Fusion"},{"key":"11730_CR31","doi-asserted-by":"publisher","unstructured":"Rai HM, Chatterjee K, Gupta A, Dubey A (2020) A novel deep cnn model for classification of brain tumor from mr images. In: 2020 IEEE 1st international conference for convergence in engineering (ICCE), pp 134\u2013138.https:\/\/doi.org\/10.1109\/ICCE50343.2020.9290740","DOI":"10.1109\/ICCE50343.2020.9290740"},{"key":"11730_CR32","doi-asserted-by":"publisher","first-page":"102477","DOI":"10.1016\/j.bspc.2021.102477","volume":"66","author":"HM Rai","year":"2021","unstructured":"Rai HM, Chatterjee K, Dashkevich S (2021) Automatic and accurate abnormality detection from brain mr images using a novel hybrid unetresnext-50 deep cnn model. Biomedical Signal Processing and Control 66:102477","journal-title":"Biomedical Signal Processing and Control"},{"issue":"6","key":"11730_CR33","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"11730_CR34","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1111\/1471-0528.14793","volume":"125","author":"L Saraswat","year":"2018","unstructured":"Saraswat L, Ayansina D, Cooper K, Bhattacharya S, Horne A, Bhattacharya S (2018) Impact of endometriosis on risk of further gynaecological surgery and cancer: a national cohort study. BJOG\u202f: An International Journal of Obstetrics & Gynaecology 125(1):64\u201372","journal-title":"BJOG : An International Journal of Obstetrics & Gynaecology"},{"key":"11730_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"1","key":"11730_CR36","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","volume":"9","author":"A Telea","year":"2004","unstructured":"Telea A (2004) An image inpainting technique based on the fast marching method. Journal of Graphics Tools 9(1):23\u201334","journal-title":"Journal of Graphics Tools"},{"issue":"1","key":"11730_CR37","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2017","unstructured":"Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N (2017) EndoNet: A deep architecture for recognition tasks on laparoscopic videos. IEEE Transactions on Medical Imaging 36(1):86\u201397. https:\/\/doi.org\/10.1109\/TMI.2016.2593957","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"11730_CR38","doi-asserted-by":"crossref","unstructured":"Visalaxi S, Punnoose D, Muthu TS (2021a) An analogy of endometriosis recognition using machine learning techniques. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV), IEEE, pp 739\u2013746","DOI":"10.1109\/ICICV50876.2021.9388403"},{"key":"11730_CR39","doi-asserted-by":"crossref","unstructured":"Visalaxi S, Punnoose D, Muthu TS (2021b) Lesion extraction of endometriotic images using open computer vision. In: 2021 international conference on artificial intelligence and smart systems (ICAIS), IEEE, pp 747\u2013751","DOI":"10.1109\/ICAIS50930.2021.9395822"},{"issue":"2","key":"11730_CR40","first-page":"2403","volume":"12","author":"S Visalaxia","year":"2021","unstructured":"Visalaxia S, Muthua TS (2021) Automated prediction of endometriosis using deep learning. Int J Nonlinear Anal Appl 12(2):2403\u20132416","journal-title":"Int J Nonlinear Anal Appl"},{"issue":"1","key":"11730_CR41","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1080\/24699322.2020.1801842","volume":"25","author":"C Yang","year":"2020","unstructured":"Yang C, Zhao Z, Hu S (2020) Image-based laparoscopic tool detection and tracking using convolutional neural networks: a review of the literature. Computer Assisted Surgery 25(1):15\u201328","journal-title":"Computer Assisted Surgery"},{"key":"11730_CR42","unstructured":"Yengera G, Mutter D, Marescaux J, Padoy N (2018) Less is more: Surgical phase recognition with less annotations through self-supervised pre-training of cnn-lstm networks. arXiv:180508569"},{"issue":"12","key":"11730_CR43","doi-asserted-by":"publisher","first-page":"5377","DOI":"10.1007\/s00464-019-07330-8","volume":"34","author":"SM Zadeh","year":"2020","unstructured":"Zadeh SM, Francois T, Calvet L, Chauvet P, Canis M, Bartoli A, Bourdel N (2020) Surgai: deep learning for computerized laparoscopic image understanding in gynaecology. Surgical Endoscopy 34(12):5377\u20135383","journal-title":"Surgical Endoscopy"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11730-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11730-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11730-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T08:13:06Z","timestamp":1645603986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11730-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["11730"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11730-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"17 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}