소장자료
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| 008 | 211202s2021 sz | s |||| 0|eng d▲ | ||
| 020 | ▼a9783030926526▼9978-3-030-92652-6▲ | ||
| 024 | 7 | ▼a10.1007/978-3-030-92652-6▼2doi▲ | |
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| 050 | 4 | ▼aTA1634▲ | |
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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 ;▼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—Digital techniques.▲ | |
| 650 | 0 | ▼aComputer vision.▲ | |
| 650 | 0 | ▼aArtificial intelligence.▲ | |
| 650 | 0 | ▼aApplication software.▲ | |
| 650 | 0 | ▼aEducation—Data 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.▼0(orcid)0000-0002-3782-9547▼1https://orcid.org/0000-0002-3782-9547▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aEgger, Jan.▼eeditor.▼0(orcid)0000-0002-5225-1982▼1https://orcid.org/0000-0002-5225-1982▼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 ;▼v13123▲ | |
| 856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-3-030-92652-6▲ |
Towards the Automatization of Cranial Implant Design in Cranioplasty II[electronic resource] : Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
자료유형
국외eBook
서명/책임사항
Towards the Automatization of Cranial Implant Design in Cranioplasty II [electronic resource] : Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / edited by Jianning Li, Jan Egger.
판사항
1st ed. 2021.
형태사항
IX, 129 p. 76 illus., 67 illus. in color. online resource.
총서사항
내용주기
Personalized 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.
요약주기
This 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.
주제
ISBN
9783030926526
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