LEADER 04570nam 22006975 450 001 9910337578003321 005 20200701111645.0 010 $a3-030-13736-8 024 7 $a10.1007/978-3-030-13736-6 035 $a(CKB)4100000007810231 035 $a(DE-He213)978-3-030-13736-6 035 $a(MiAaPQ)EBC5924089 035 $a(PPN)235231762 035 $a(EXLCZ)994100000007810231 100 $a20190313d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Methods and Clinical Applications for Spine Imaging$b[electronic resource] $e5th International Workshop and Challenge, CSI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers /$fedited by Guoyan Zheng, Daniel Belavy, Yunliang Cai, Shuo Li 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (X, 181 p. 103 illus., 77 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11397 311 $a3-030-13735-X 327 $aSpinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU -- Predicting Scoliosis in DXA Scans Using Intermediate Representations -- Fast Registration of CT with Intra-operative Ultrasound Images for Spine Surgery -- Automated Grading of Modic Changes Using CNNs ? Improving the Performance with Mix-up -- Error Estimation for Appearance Model Segmentation of Musculoskeletal Structures Using Multiple, Independent Sub-models -- Automated Segmentation of Intervertebral Disc using Fully Dilated Separable Deep Neural Networks -- Intensity Standardization of Skeleton in Follow-up Whole-Body MRI -- Towards a Deformable Multi-Surface Approach to Ligamentous Spine Models for Predictive Simulation-Based Scoliosis Surgery Planning -- Intervertebral Disc Segmentation Using Mathematical Morphology?A CNN-Free Approach. 330 $aThis book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 8 full papers presented together with 8 short papers and 1 keynote were carefully reviewed and selected for inclusion in this volume. Papers on novel methodology and clinical research, and also papers which demonstrate the performance of methods on the provided challenges, the aim is to cover both theoretical and very practical aspects of computerized spinal imaging. . 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11397 606 $aOptical data processing 606 $aHealth informatics 606 $aComputer communication systems 606 $aArtificial intelligence 606 $aComputer hardware 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Hardware$3https://scigraph.springernature.com/ontologies/product-market-codes/I1200X 615 0$aOptical data processing. 615 0$aHealth informatics. 615 0$aComputer communication systems. 615 0$aArtificial intelligence. 615 0$aComputer hardware. 615 14$aImage Processing and Computer Vision. 615 24$aHealth Informatics. 615 24$aComputer Communication Networks. 615 24$aArtificial Intelligence. 615 24$aComputer Hardware. 676 $a616.730754 702 $aZheng$b Guoyan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBelavy$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCai$b Yunliang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Shuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337578003321 996 $aComputational Methods and Clinical Applications for Spine Imaging$91959956 997 $aUNINA