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024 7 _a10.1007/978-3-030-92652-6
_2doi
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050 4 _aTA1634
072 7 _aUYT
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072 7 _aCOM016000
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245 1 0 _aTowards the Automatization of Cranial Implant Design in Cranioplasty II
_h[electronic resource] :
_bSecond Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /
_cedited by Jianning Li, Jan Egger.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aIX, 129 p. 76 illus., 67 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v13123
505 0 _aPersonalized Calvarial Reconstruction in Neurosurgery -- Qualitative Criteria for Designing Feasible Cranial Implants -- Segmentation of Defective Skulls from CT Data for Tissue Modelling -- Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets -- Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation -- A U-Net based System for Cranial Implant Design with Pre-processing and Learned Implant Filtering -- Sparse Convolutional Neural Network for Skull Reconstruction -- Cranial Implant Prediction by Learning an Ensemble of Slice-based Skull Completion networks -- PCA-Skull: 3D Skull Shape Modelling Using Principal Component Analysis -- Cranial Implant Design using V-Net based Region of Interest Reconstruction.
520 _aThis book constitutes the Second Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in September, 2021. The challenge took place virtually due to the COVID-19 pandemic. The 7 papers are presented together with one invited paper, one qualitative evaluation criteria from neurosurgeons and a dataset descriptor. This challenge aims to provide more affordable, faster, and more patient-friendly solutions to the design and manufacturing of medical implants, including cranial implants, which is needed in order to repair a defective skull from a brain tumor surgery or trauma. The presented solutions can serve as a good benchmark for future publications regarding 3D volumetric shape learning and cranial implant design.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 0 _aApplication software.
650 0 _aEducation
_xData processing.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aComputers and Education.
700 1 _aLi, Jianning.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aEgger, Jan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030926519
776 0 8 _iPrinted edition:
_z9783030926533
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v13123
856 4 0 _uhttps://doi.org/10.1007/978-3-030-92652-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c178669
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