{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:29:35Z","timestamp":1730309375814,"version":"3.28.0"},"reference-count":15,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,2]]},"DOI":"10.1117\/12.2292731","type":"proceedings-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T21:42:32Z","timestamp":1520026952000},"page":"7","source":"Crossref","is-referenced-by-count":3,"title":["Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images"],"prefix":"10.1117","author":[{"given":"Ivana","family":"I\u0161gum","sequence":"first","affiliation":[]},{"given":"Bram","family":"van Ginneken","sequence":"first","affiliation":[]},{"given":"Nikolas","family":"Lessmann","sequence":"first","affiliation":[]}],"member":"189","reference":[{"key":"c1","first-page":"590","article-title":"Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans","author":"Glocker","year":"2012"},{"key":"c2","first-page":"515","article-title":"Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks","author":"Chen","year":"2015"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0143327"},{"key":"c4","first-page":"433","article-title":"Model-based segmentation of vertebral bodies from MR images with 3D CNNs","volume":"9901","author":"Korez","year":"2016"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55050-3"},{"article-title":"A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets","year":"2017","author":"Sekuboyina","key":"c6"},{"key":"c7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2548501"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"c9","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"key":"c10","first-page":"633","article-title":"Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization","volume":"10265","author":"Yang","year":"2017"},{"key":"c11","first-page":"234","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","volume":"9901","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"c12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.12.006"},{"key":"c13","unstructured":"Theano Development Team, \u201cTheano: A Python framework for fast computation of mathematical expressions.\u201d arXiv:1605.02688 (2016)."},{"key":"c14","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"Kingma","key":"c15"}],"event":{"name":"Image Processing","start":{"date-parts":[[2018,2,10]]},"location":"Houston, United States","end":{"date-parts":[[2018,2,15]]}},"container-title":["Medical Imaging 2018: Image Processing"],"original-title":[],"deposited":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T22:08:23Z","timestamp":1527113303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/10574\/2292731\/Iterative-convolutional-neural-networks-for-automatic-vertebra-identification-and-segmentation\/10.1117\/12.2292731.full"}},"subtitle":[],"editor":[{"given":"Elsa D.","family":"Angelini","sequence":"first","affiliation":[]},{"given":"Bennett A.","family":"Landman","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,3,2]]},"references-count":15,"URL":"https:\/\/doi.org\/10.1117\/12.2292731","relation":{},"subject":[],"published":{"date-parts":[[2018,3,2]]}}}