{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:01:50Z","timestamp":1743127310540,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031485923"},{"type":"electronic","value":"9783031485930"}],"license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"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\/978-3-031-48593-0_17","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T05:03:03Z","timestamp":1701406983000},"page":"231-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Web-Based AI System for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Taowen","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Songyun","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Leyang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yiqi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sihan","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Jacqueline","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Ahmed E.","family":"Fetit","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"17_CR1","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Cabrera, Y., Fetit, A.E.: Reducing CNN textural bias with k-space artifacts improves robustness. IEEE Access 10 (2022)","DOI":"10.1109\/ACCESS.2022.3179844"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ben Ayed, I.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans. Med. Imaging 38(5) (2019)","DOI":"10.1109\/TMI.2018.2878669"},{"key":"17_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.572068","volume":"14","author":"A Gherman","year":"2020","unstructured":"Gherman, A., Muschelli, J., Caffo, B., Crainiceanu, C.: Rxnat: an open-source R package for XNAT-based repositories. Front. Neuroinform. 14, 572068 (2020)","journal-title":"Front. Neuroinform."},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1016\/j.neuroimage.2015.05.074","volume":"124","author":"DN Kennedy","year":"2016","unstructured":"Kennedy, D.N., Haselgrove, C., Riehl, J., Preuss, N., Buccigrossi, R.: The NITRC image repository. Neuroimage 124, 1069\u20131073 (2016)","journal-title":"Neuroimage"},{"key":"17_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-01600-y","volume":"44","author":"M Khvastova","year":"2020","unstructured":"Khvastova, M., Witt, M., Essenwanger, A., Sass, J., Thun, S., Krefting, D.: Towards interoperability in clinical research-enabling FHIR on the open-source research platform XNAT. J. Med. Syst. 44, 1\u20135 (2020)","journal-title":"J. Med. Syst."},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Li, S., Ke, L., Pratama, K., Tai, Y.W., Tang, C.K., Cheng, K.T.: Cascaded deep monocular 3D human pose estimation with evolutionary training data. In: 2020 IEEE\/CVF CVPR, June 2020","DOI":"10.1109\/CVPR42600.2020.00621"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neuroimage.2018.01.054","volume":"173","author":"A Makropoulos","year":"2018","unstructured":"Makropoulos, A., et al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88\u2013112 (2018)","journal-title":"Neuroimage"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Marcus, D.S., Olsen, T.R., Ramaratnam, M., Buckner, R.L.: The extensible neuroimaging archive toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5(1) (2007)","DOI":"10.1385\/NI:5:1:11"},{"key":"17_CR10","unstructured":"Moore, C.M.: Nifti (File format) $$|$$ radiology reference article $$|$$ radiopaedia.org"},{"key":"17_CR11","unstructured":"Nikolaos, A.M.: Deep learning in medical image analysis : a comparative analysis of multi-modal brain-MRI segmentation with 3D deep neural networks, July 2019"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fninf.2012.00012","volume":"6","author":"Y Schwartz","year":"2012","unstructured":"Schwartz, Y., et al.: PyXNAT: XNAT in python. Front. Neuroinform. 6, 12 (2012)","journal-title":"Front. Neuroinform."},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition (2017)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s10278-015-9834-0","volume":"29","author":"F Valente","year":"2016","unstructured":"Valente, F., Silva, L.A.B., Godinho, T.M., Costa, C.: Anatomy of an extensible open source PACS. J. Digit. Imaging 29, 284\u2013296 (2016)","journal-title":"J. Digit. Imaging"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"issue":"3","key":"17_CR16","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1093\/neuonc\/noac189","volume":"25","author":"P Vollmuth","year":"2023","unstructured":"Vollmuth, P., et al.: Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: an international multi-reader study. Neuro Oncol. 25(3), 533\u2013543 (2023)","journal-title":"Neuro Oncol."},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1200\/CCI.19.00131","volume":"4","author":"E Ziegler","year":"2020","unstructured":"Ziegler, E., et al.: Open health imaging foundation viewer: an extensible open-source framework for building web-based imaging applications to support cancer research. JCO Clin. Cancer Inform. 4, 336\u2013345 (2020)","journal-title":"JCO Clin. Cancer Inform."}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48593-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T05:11:43Z","timestamp":1701407503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48593-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"ISBN":["9783031485923","9783031485930"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48593-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"2 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberdeen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"19 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.abdn.ac.uk\/events\/conferences\/miua2023","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"24","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":"57% - 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":"2-3","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":"4","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)"}}]}}