{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T05:23:03Z","timestamp":1745817783374,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031524479"},{"type":"electronic","value":"9783031524486"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-52448-6_10","type":"book-chapter","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T07:03:34Z","timestamp":1706771014000},"page":"98-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Quality-Controlled Left Heart Segmentation from\u00a02D Echocardiography"],"prefix":"10.1007","author":[{"given":"Bram W. M.","family":"Geven","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debbie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen A.","family":"Creamer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua R.","family":"Dillon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gina M.","family":"Quill","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola C.","family":"Edwards","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malcolm E.","family":"Legget","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert N.","family":"Doughty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alistair A.","family":"Young","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiranja P. Babarenda","family":"Gamage","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martyn P.","family":"Nash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop. vol. 10, pp. 359\u2013370. Seattle, WA, USA (1994)","key":"10_CR1"},{"doi-asserted-by":"publisher","unstructured":"Folland, E.D., Parisi, A.F., Moynihan, P.F., Jones, D.R., Feldman, C.L., Tow, D.E.: Assessment of left ventricular ejection fraction and volumes by real-time, two-dimensional echocardiography. A comparison of cineangiographiy and radionuclide techniques. Circulation 60(4), 760\u2013766 (1979). https:\/\/doi.org\/10.1161\/01.cir.60.4.760","key":"10_CR2","DOI":"10.1161\/01.cir.60.4.760"},{"key":"10_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-030-80432-9_22","volume-title":"Medical Image Understanding and Analysis","author":"E Hann","year":"2021","unstructured":"Hann, E., Gonzales, R.A., Popescu, I.A., Zhang, Q., Ferreira, V.M., Piechnik, S.K.: Ensemble of deep convolutional neural networks with Monte Carlo dropout sampling for automated image segmentation quality control and robust deep learning using small datasets. In: Papie\u017c, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) MIUA 2021. LNCS, vol. 12722, pp. 280\u2013293. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80432-9_22"},{"issue":"2","key":"10_CR4","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., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat. Methods"},{"doi-asserted-by":"publisher","unstructured":"Keshavan, A., Datta, E., M. McDonough, I., Madan, C.R., Jordan, K., Henry, R.G.: Mindcontrol: a web application for brain segmentation quality control. NeuroImage 170, 365\u2013372 (2018). https:\/\/doi.org\/10.1016\/j.neuroimage.2017.03.055","key":"10_CR5","DOI":"10.1016\/j.neuroimage.2017.03.055"},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155\u2013163 (2016). https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J. Chiropr. Med."},{"doi-asserted-by":"publisher","unstructured":"Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198\u20132210 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2900516","key":"10_CR7","DOI":"10.1109\/TMI.2019.2900516"},{"unstructured":"Leclerc, S., et al.: Deep learning segmentation in 2D echocardiography using the CAMUS dataset: automatic assessment of the anatomical shape validity. In: International Conference on Medical Imaging with Deep Learning - Extended Abstract Track, London, United Kingdom (2019)","key":"10_CR8"},{"doi-asserted-by":"publisher","unstructured":"Meyers, B., Brindise, M., Kutty, S., Vlachos, P.: A method for direct estimation of left ventricular global longitudinal strain rate from echocardiograms. Sci. Rep. 12(1), 4008 (2022). https:\/\/doi.org\/10.1038\/s41598-022-06878-1","key":"10_CR9","DOI":"10.1038\/s41598-022-06878-1"},{"issue":"11","key":"10_CR10","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1109\/TMI.2020.3003240","volume":"39","author":"N Painchaud","year":"2020","unstructured":"Painchaud, N., Skandarani, Y., Judge, T., Bernard, O., Lalande, A., Jodoin, P.M.: Cardiac segmentation with strong anatomical guarantees. IEEE Trans. Med. Imaging 39(11), 3703\u20133713 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3003240","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"publisher","unstructured":"Robinson, R., et al.: Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study. J. Cardiovasc Magn. Reson. 21(1), 18 (2019). https:\/\/doi.org\/10.1186\/s12968-019-0523-x","key":"10_CR11","DOI":"10.1186\/s12968-019-0523-x"},{"key":"10_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-030-00937-3_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Robinson","year":"2018","unstructured":"Robinson, R., et al.: Real-time prediction of segmentation quality. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 578\u2013585. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_66"},{"key":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/978-3-030-00928-1_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy, A.G., Conjeti, S., Navab, N., Wachinger, C.: Inherent brain segmentation quality control from fully ConvNet Monte Carlo sampling. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 664\u2013672. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_75"},{"issue":"3","key":"10_CR14","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.jcmg.2019.05.030","volume":"13","author":"B Ruijsink","year":"2020","unstructured":"Ruijsink, B., et al.: Fully automated, quality-controlled cardiac analysis from CMR. JACC Cardiovasc. Imaging 13(3), 684\u2013695 (2020). https:\/\/doi.org\/10.1016\/j.jcmg.2019.05.030","journal-title":"JACC Cardiovasc. Imaging"},{"doi-asserted-by":"publisher","unstructured":"Smiseth, O.A., Donal, E., Penicka, M., Sletten, O.J.: How to measure left ventricular myocardial work by pressure-strain loops. Eur. Heart J. Cardiovasc. Imaging 22(3), 259\u2013261 (2020). https:\/\/doi.org\/10.1093\/ehjci\/jeaa301","key":"10_CR15","DOI":"10.1093\/ehjci\/jeaa301"},{"doi-asserted-by":"publisher","unstructured":"Thrall, J.H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., Brink, J.: Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J. Am. Coll. Radiol. 15(3, Part B), 504\u2013508 (2018). https:\/\/doi.org\/10.1016\/j.jacr.2017.12.026","key":"10_CR16","DOI":"10.1016\/j.jacr.2017.12.026"},{"doi-asserted-by":"publisher","unstructured":"Vallat, R.: Pingouin: statistics in Python. J. Open Source Softw. 3(31), 1026 (2018). https:\/\/doi.org\/10.21105\/joss.01026","key":"10_CR17","DOI":"10.21105\/joss.01026"},{"key":"10_CR18","volume-title":"Python 3 Reference Manual","author":"G Van Rossum","year":"2009","unstructured":"Van Rossum, G., Drake, F.L.: Python 3 Reference Manual. CreateSpace, Scotts Valley, CA (2009)"},{"doi-asserted-by":"publisher","unstructured":"Virtanen, P., et al.: SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261\u2013272 (2020). https:\/\/doi.org\/10.1038\/s41592-019-0686-2","key":"10_CR19","DOI":"10.1038\/s41592-019-0686-2"},{"key":"10_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s10554-022-02554-7","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset. Int. J. Cardiovasc. Imaging (2022). https:\/\/doi.org\/10.1007\/s10554-022-02554-7","journal-title":"Int. J. Cardiovasc. Imaging"}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-52448-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T07:05:02Z","timestamp":1706771102000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-52448-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031524479","9783031524486"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-52448-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Our code is publicly available on GitHub:","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}},{"value":"STACOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Statistical Atlases and Computational Models of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/stacom.github.io\/stacom2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}