{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:49:51Z","timestamp":1742921391521,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811636363"},{"type":"electronic","value":"9789811636370"}],"license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"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":[[2022]]},"DOI":"10.1007\/978-981-16-3637-0_5","type":"book-chapter","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T11:58:23Z","timestamp":1633089503000},"page":"59-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Spatio-Temporal Deep Learning-Based Approach for Cardiac Cine MRI Segmentation"],"prefix":"10.1007","author":[{"given":"Abderazzak","family":"Ammar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Bouattane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Youssfi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation (2018). arXiv:1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M.A., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V.A., Krishnamurthi, G., Rohe, M.M., Pennec, X., Sermesant, M., Isensee, F., Jager, P., Maier-Hein, K.H., Full, P.M., Wolf, I., Engelhardt, S., Baum- gartner, C.F., Koch, L.M., Wolterink, J.M., Isgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., Jodoin, P.M.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37, 2514\u20132525 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2837502","DOI":"10.1109\/TMI.2018.2837502"},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JBHI.2018.2852635","volume":"23","author":"A Chakravarty","year":"2019","unstructured":"Chakravarty, A., Sivaswamy, J.: RACE-Net: a recurrent neural network for biomedical image segmentation. IEEE J. Biomed. Health Inform. 23, 1151\u20131162 (2019). doi: 10.1109\/JBHI.2018.2852635","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Gers, F., Schmidhuber, J.: Recurrent nets that time and count. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, pp. 189\u2013194, vol.3. IEEE (2000). https:\/\/doi.org\/10.1109\/IJCNN.2000.861302","DOI":"10.1109\/IJCNN.2000.861302"},{"key":"5_CR5","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors, 1\u201318 (2012). arXiv:1207.0580"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997). doi: 10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"5_CR7","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning, ICML 2015 1, 448\u2013456 (2015). arXiv:1502.03167"},{"key":"5_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings abs\/1412.6 (2014). arXiv:1412.6980"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015). DOI 10.1038\/nature14539, arXiv:1807.07987","DOI":"10.1038\/nature14539"},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"J Long","year":"2014","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640\u2013651 (2014). doi: 10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR11","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML 2010\u2014Proceedings, 27th International Conference on Machine Learning, 807\u2013814 (2010). URL https:\/\/icml.cc\/Conferences\/2010\/papers\/432.pdf"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Poudel, R.P.K., Lamata, P., Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10129 LNCS, pp. 83\u201394 (2017). DOI 10.1007\/978-3-319-52280-7\\_8","DOI":"10.1007\/978-3-319-52280-7_8"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation 9351, 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"5_CR14","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802\u2013810 (2015). arXiv:1506.04214"},{"key":"5_CR15","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.:. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014). URL http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"key":"5_CR16","unstructured":"World Health Organization.: Cardiovascular diseases (CVDs) (2017). URL https:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2528\u20132535 (2010). https:\/\/doi.org\/10.1109\/CVPR.2010.5539957","DOI":"10.1109\/CVPR.2010.5539957"}],"container-title":["Smart Innovation, Systems and Technologies","Networking, Intelligent Systems and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3637-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T12:14:32Z","timestamp":1633090472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3637-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,2]]},"ISBN":["9789811636363","9789811636370"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3637-0_5","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2021,10,2]]},"assertion":[{"value":"2 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}