{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:36:08Z","timestamp":1743104168823,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030891275"},{"type":"electronic","value":"9783030891282"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89128-2_14","type":"book-chapter","created":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T03:02:47Z","timestamp":1635649367000},"page":"145-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6187-720X","authenticated-orcid":false,"given":"Khouloud","family":"Boukhris","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4271-3506","authenticated-orcid":false,"given":"Ramzi","family":"Mahmoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-7734","authenticated-orcid":false,"given":"Asma Ben","family":"Abdallah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0870-0736","authenticated-orcid":false,"given":"Mabrouk","family":"AbdelAli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-1331","authenticated-orcid":false,"given":"Badii","family":"Hmida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4846-1722","authenticated-orcid":false,"given":"Mohamed H\u00e9di","family":"Bedoui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"issue":"2","key":"14_CR1","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.media.2010.12.004","volume":"15","author":"C Petitjean","year":"2011","unstructured":"Petitjean, C., Dacher, J.-N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169\u2013184 (2011). https:\/\/doi.org\/10.1016\/j.media.2010.12.004","journal-title":"Med. Image Anal."},{"issue":"1","key":"14_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1161\/01.CIR.76.1.44","volume":"76","author":"HD White","year":"1987","unstructured":"White, H.D., Norris, R.M., Brown, M.A., Brandt, P.W., Whitlock, R.M., Wild, C.J.: Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 76(1), 44\u201351 (1987). https:\/\/doi.org\/10.1161\/01.CIR.76.1.44","journal-title":"Circulation"},{"issue":"5","key":"14_CR3","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1109\/TMI.2005.843740","volume":"24","author":"C Pluempitiwiriyawej","year":"2005","unstructured":"Pluempitiwiriyawej, C., Moura, J.M.F., Lin Wu, Y.-J., Ho, C.: STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans. Med. Imaging 24(5), 593\u2013603 (2005). https:\/\/doi.org\/10.1109\/TMI.2005.843740","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6(Part 1)","key":"14_CR4","doi-asserted-by":"publisher","first-page":"2741","DOI":"10.1118\/1.4947126","volume":"43","author":"C Feng","year":"2016","unstructured":"Feng, C., Zhang, S., Zhao, D., Li, C.: Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Med. Phys. 43(6(Part 1)), 2741\u20132755 (2016)","journal-title":"Med. Phys."},{"issue":"11","key":"14_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"14_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation, pp. 3431\u20133440 (2015). Accessed 28 Oct 2020","key":"14_CR7","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"100297","DOI":"10.1016\/j.imu.2020.100297","volume":"18","author":"I Rizwan","year":"2020","unstructured":"Rizwan, I., Haque, I., Neubert, J.: Deep learning approaches to biomedical image segmentation. Inform. Med. Unlocked 18, 100297 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"14_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-319-75541-0_17","volume-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges","author":"Y Jang","year":"2018","unstructured":"Jang, Y., Hong, Y., Ha, S., Kim, S., Chang, H.-J.: Automatic segmentation of LV and RV in cardiac MRI. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 161\u2013169. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75541-0_17"},{"key":"14_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-319-75541-0_15","volume-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges","author":"M Khened","year":"2018","unstructured":"Khened, M., Alex, V., Krishnamurthi, G.: Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 140\u2013151. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75541-0_15"},{"key":"14_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-319-75541-0_13","volume-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120\u2013129. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75541-0_13"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"103356","DOI":"10.1016\/j.compbiomed.2019.103356","volume":"111","author":"W Yan","year":"2019","unstructured":"Yan, W., Wang, Y., van der Geest, R.J., Tao, Q.: Cine MRI analysis by deep learning of optical flow: adding the temporal dimension. Comput. Biol. Med. 111, 103356 (2019). https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103356","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"14_CR13","doi-asserted-by":"publisher","first-page":"541","DOI":"10.3233\/XST-190621","volume":"28","author":"Y He","year":"2020","unstructured":"He, Y., et al.: Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network. J. X-Ray Sci. Technol. 28(3), 541\u2013553 (2020)","journal-title":"J. X-Ray Sci. Technol."},{"issue":"6","key":"14_CR14","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1109\/JSTSP.2020.3013351","volume":"14","author":"G Simantiris","year":"2020","unstructured":"Simantiris, G., Tziritas, G.: Cardiac MRI segmentation with a dilated CNN incorporating domain-specific constraints. IEEE J. Sel. Top. Signal Process. 14(6), 1235\u20131243 (2020). https:\/\/doi.org\/10.1109\/JSTSP.2020.3013351","journal-title":"IEEE J. Sel. Top. Signal Process."},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks, pp. 4700\u20134708 (2017). Accessed 01 May 2021","key":"14_CR15","DOI":"10.1109\/CVPR.2017.243"},{"issue":"12","key":"14_CR16","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network, pp. 2881\u20132890 (2017). Accessed 01 May 2021","key":"14_CR17","DOI":"10.1109\/CVPR.2017.660"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition, pp. 770\u2013778 (2016). Accessed 02 May 2021","key":"14_CR18","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"92539","DOI":"10.1109\/ACCESS.2019.2925060","volume":"7","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Du, J., Liu, H., Hou, X., Zhao, Y., Ding, M.: LU-NET: an improved U-Net for ventricular segmentation. IEEE Access 7, 92539\u201392546 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2925060","journal-title":"IEEE Access"},{"issue":"11","key":"14_CR20","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2837502","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR21","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/B978-0-12-336156-1.50061-6","volume":"4","author":"K Zuiderveld","year":"1994","unstructured":"Zuiderveld, K.: Contrast limited adaptive histogram equalization. Graph. Gems 4, 474\u2013485 (1994)","journal-title":"Graph. Gems"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017). https:\/\/arxiv.org\/abs\/1412.6980. Accessed 03 May 2021","key":"14_CR22"}],"container-title":["Lecture Notes in Computer Science","Computer Analysis of Images and Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89128-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T03:04:16Z","timestamp":1635649456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89128-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030891275","9783030891282"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89128-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Analysis of Images and Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cyprusconferences.org\/caip2021\/","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":"EasyAcademia","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","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":"87","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":"67% - 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":"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)"}}]}}