{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T08:47:30Z","timestamp":1771663650783,"version":"3.50.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319522791","type":"print"},{"value":"9783319522807","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-52280-7_9","type":"book-chapter","created":{"date-parts":[[2017,1,18]],"date-time":"2017-01-18T10:15:26Z","timestamp":1484734526000},"page":"95-102","source":"Crossref","is-referenced-by-count":45,"title":["Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease"],"prefix":"10.1007","author":[{"given":"Jelmer M.","family":"Wolterink","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Leiner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max A.","family":"Viergever","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivana","family":"I\u0161gum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,1,19]]},"reference":[{"key":"9_CR1","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). In: ICLR (2016)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Gilboa, S.M., Devine, O.J., Kucik, J.E., Oster, M.E., Riehle-Colarusso, T., Nembhard, W.N., Xu, P., Correa, A., Jenkins, K., Marelli, A.J.: Congenital heart defects in the United States: Estimating the magnitude of the affected population in 2010. Circulation 134(2), 101\u2013109 (2016)","DOI":"10.1161\/CIRCULATIONAHA.115.019307"},{"key":"9_CR3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Imag. Anal. 35, 18\u201331 (2017)","journal-title":"Med. Imag. Anal."},{"key":"9_CR4","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)"},{"key":"9_CR5","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"issue":"5","key":"9_CR6","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TMI.2016.2548501","volume":"35","author":"P Moeskops","year":"2016","unstructured":"Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., I\u0161gum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imag. 35(5), 1252\u20131261 (2016)","journal-title":"IEEE Trans. Med. Imag."},{"key":"9_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-319-24574-4_10","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"DF Pace","year":"2015","unstructured":"Pace, D.F., Dalca, A.V., Geva, T., Powell, A.J., Moghari, M.H., Golland, P.: Interactive whole-heart segmentation in congenital heart disease. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 80\u201388. Springer, Heidelberg (2015). doi: 10.1007\/978-3-319-24574-4_10"},{"key":"9_CR8","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, Heidelberg (2015). doi: 10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"9_CR9","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1093\/ejcts\/ezu310","volume":"47","author":"D Schmauss","year":"2015","unstructured":"Schmauss, D., Haeberle, S., Hagl, C., Sodian, R.: Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience. Eur. J. Cardiothorac. Surg. 47(6), 1044\u20131052 (2015)","journal-title":"Eur. J. Cardiothorac. Surg."},{"issue":"1","key":"9_CR10","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","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(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"04","key":"9_CR11","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1017\/S1047951114000742","volume":"25","author":"I Valverde","year":"2015","unstructured":"Valverde, I., Gomez, G., Gonzalez, A., Suarez-Mejias, C., Adsuar, A., Coserria, J.F., Uribe, S., Gomez-Cia, T., Hosseinpour, A.R.: Three-dimensional patient-specific cardiac model for surgical planning in Nikaidoh procedure. Cardiol. Young 25(04), 698\u2013704 (2015)","journal-title":"Cardiol. Young"},{"key":"9_CR12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.media.2016.04.004","volume":"34","author":"JM Wolterink","year":"2016","unstructured":"Wolterink, J.M., Leiner, T., de Vos, B.D., van Hamersvelt, R.W., Viergever, M.A., I\u0161gum, I.: Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med. Imag. Anal. 34, 123\u2013136 (2016)","journal-title":"Med. Imag. Anal."},{"key":"9_CR13","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)"},{"key":"9_CR14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Imag. Anal. 31, 77\u201387 (2016)","journal-title":"Med. Imag. Anal."}],"container-title":["Lecture Notes in Computer Science","Reconstruction, Segmentation, and Analysis of Medical Images"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-52280-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,25]],"date-time":"2017-06-25T04:32:36Z","timestamp":1498365156000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-52280-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319522791","9783319522807"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-52280-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}