{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:27:21Z","timestamp":1743028041794,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031476648"},{"type":"electronic","value":"9783031476655"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-47665-5_4","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:01:39Z","timestamp":1699128099000},"page":"41-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knee Osteoarthritis Diagnostic System Based on 3D Multi-task Convolutional Neural Network: Data from the Osteoarthritis Initiative"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8602-0533","authenticated-orcid":false,"given":"Khin Wee","family":"Lai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-4479","authenticated-orcid":false,"given":"Pauline Shan Qing","family":"Yeoh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-1196","authenticated-orcid":false,"given":"Siew Li","family":"Goh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0471-3820","authenticated-orcid":false,"given":"Khairunnisa","family":"Hasikin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-9781","authenticated-orcid":false,"given":"Xiang","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s11517-017-1710-2","volume":"56","author":"A Faisal","year":"2018","unstructured":"Faisal, A., Ng, S.-C., Goh, S.-L., Lai, K.W.: Knee cartilage segmentation and thickness computation from ultrasound images. Med. Biol. Eng. Comput. 56, 657\u2013669 (2018). https:\/\/doi.org\/10.1007\/s11517-017-1710-2","journal-title":"Med. Biol. Eng. Comput."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"981","DOI":"10.2174\/1573405616666201214122409","volume":"17","author":"CW Yong","year":"2021","unstructured":"Yong, C.W., Lai, K.W., Murphy, B.P., Hum, Y.C.: Comparative study of encoder-decoder-based convolutional neural networks in cartilage delineation from knee magnetic resonance images. Curr. Med. Imaging 17, 981\u2013987 (2021). https:\/\/doi.org\/10.2174\/1573405616666201214122409","journal-title":"Curr. Med. Imaging"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"182347","DOI":"10.1109\/ACCESS.2020.3028390","volume":"8","author":"S Anis","year":"2020","unstructured":"Anis, S., et al.: An overview of deep learning approaches in chest radiograph. IEEE Access 8, 182347\u2013182354 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3028390","journal-title":"IEEE Access"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Yeoh, P.S.Q., et al.: Emergence of deep learning in knee osteoarthritis diagnosis. Comput. Intell. Neurosci. 2021 (2021). https:\/\/doi.org\/10.1155\/2021\/4931437","DOI":"10.1155\/2021\/4931437"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Sakuma, I., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2013, LNCS, vol. 8150, pp. 246\u2013253. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_31","DOI":"10.1007\/978-3-642-40763-5_31"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1016\/j.joca.2019.02.800","volume":"27","author":"V Pedoia","year":"2019","unstructured":"Pedoia, V., Lee, J., Norman, B., Link, T.M., Majumdar, S.: Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire osteoarthritis Initiative baseline cohort. Osteoarthritis Cartilage 27, 1002\u20131010 (2019). https:\/\/doi.org\/10.1016\/j.joca.2019.02.800","journal-title":"Osteoarthritis Cartilage"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Teoh, Y.X., et al.: Discovering knee osteoarthritis imaging features for diagnosis and prognosis: review of manual imaging grading and machine learning approaches. J. Healthc. Eng. 2022 (2022). https:\/\/doi.org\/10.1155\/2022\/4138666","DOI":"10.1155\/2022\/4138666"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1002\/mrm.27229","volume":"80","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Zhao, G., Kijowski, R., Liu, F.: Deep convolutional neural network for segmentation of knee joint anatomy. Magn. Reson. Med. 80, 2759\u20132770 (2018). https:\/\/doi.org\/10.1002\/mrm.27229","journal-title":"Magn. Reson. Med."},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Tack, A., Zachow, S.: Accurate automated volumetry of cartilage of the knee using convolutional neural networks: data from the osteoarthritis initiative. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 40\u201343. IEEE, New York (2019). https:\/\/doi.org\/10.1109\/ISBI.2019.8759201","DOI":"10.1109\/ISBI.2019.8759201"},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.media.2018.11.009","volume":"52","author":"F Ambellan","year":"2019","unstructured":"Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the Osteoarthritis Initiative. Med. Image Anal. 52, 109\u2013118 (2019). https:\/\/doi.org\/10.1016\/j.media.2018.11.009","journal-title":"Med. Image Anal."},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-63395-9","volume":"10","author":"AA Tolpadi","year":"2020","unstructured":"Tolpadi, A.A., Lee, J.J., Pedoia, V., Majumdar, S.: Deep learning predicts total knee replacement from magnetic resonance images. Sci. Rep. 10, 1\u201312 (2020). https:\/\/doi.org\/10.1038\/s41598-020-63395-9","journal-title":"Sci. Rep."},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"S386","DOI":"10.1016\/j.joca.2019.02.386","volume":"27","author":"AM Martinez","year":"2019","unstructured":"Martinez, A.M., et al.: Discovering knee osteoarthritis bone shape features using deep learning. Osteoarthritis Cartilage 27, S386\u2013S387 (2019). https:\/\/doi.org\/10.1016\/j.joca.2019.02.386","journal-title":"Osteoarthritis Cartilage"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Yeoh, P.S.Q., Lai, K.W., Goh, S.L., Hasikin, K., Wu, X., Li, P.: Transfer learning assisted 3D deep learning models for knee osteoarthritis detection: data from the osteoarthritis initiative. Front. Bioeng. Biotechnol. 11 (2023). https:\/\/doi.org\/10.3389\/fbioe.2023.1164655","DOI":"10.3389\/fbioe.2023.1164655"},{"key":"4_CR14","doi-asserted-by":"publisher","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: classification and segmentation. Comput. Biol. Med. 126, 104037 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104037","journal-title":"Comput. Biol. Med."},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Liu, M., et al.: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer\u2019s disease. Neuroimage 208, 116459 (2020). https:\/\/doi.org\/10.1016\/j.neuroimage.2019.116459","DOI":"10.1016\/j.neuroimage.2019.116459"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE, New York (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR17","unstructured":"Sifre, L.: Rigid-motion scattering for image classification. Ecole Polytechniq. (2014)"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE, New York (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4_CR19","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32 (NIPS 2019), pp. 8024\u20138035. NIPS, California (2019)"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 30th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261\u20132269. IEEE, New York (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987\u20135995. IEEE, New York (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014). https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016, LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth IEEE International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE, New York (2016). https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"4_CR25","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1088\/0031-9155\/60\/10\/4015","volume":"60","author":"Z Jahanzad","year":"2015","unstructured":"Jahanzad, Z., et al.: Regional assessment of LV wall in infarcted heart using tagged MRI and cardiac modelling. Phys. Med. Biol. 60, 4015 (2015). https:\/\/doi.org\/10.1088\/0031-9155\/60\/10\/4015","journal-title":"Phys. Med. Biol."},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"41497","DOI":"10.1007\/s11042-021-10557-0","volume":"81","author":"CW Yong","year":"2021","unstructured":"Yong, C.W., et al.: Knee osteoarthritis severity classification with ordinal regression module. Multim. Tools Appl. 81, 41497\u201341509 (2021). https:\/\/doi.org\/10.1007\/s11042-021-10557-0","journal-title":"Multim. Tools Appl."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47665-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:10:32Z","timestamp":1699128632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47665-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031476648","9783031476655"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47665-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"5 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ericlab.org\/acpr2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","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":"93","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","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}