Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition (Record no. 87470)
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fixed length control field | 04348nam a22006375i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-53241-2 |
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control field | 20240730171240.0 |
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ISBN | 9783031532412 |
-- | 978-3-031-53241-2 |
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Call Number | 006 |
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Title | Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition |
Sub Title | First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2024. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XII, 142 p. 74 illus., 67 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Lecture Notes in Computer Science, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography -- Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge -- A Data-Centric Approach for Segmenting the Aortic Vessel Tree: A Solution to SEG.A. Challenge 2023 Segmentation Task -- Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge -- Position-encoded pixel-to-prototype contrastive learning for aortic vessel tree segmentation -- Misclassification Loss for Segmentation of the Aortic Vessel Tree -- Deep Learning-based segmentation and mesh reconstruction of the Aortic Vessel Tree from CTA images -- RASNet: U-Net-based Robust Aortic Segmentation Network For Multicenter Datasets -- Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis -- A mini tutorial on mesh generation of blood vessels for CFD applications. |
520 ## - SUMMARY, ETC. | |
Summary, etc | This book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, on October 8, 2023. The 8 full and 3 short papers presented have been carefully reviewed and selected for inclusion in the book. They focus specifically on robustness, visual quality and meshing of automatically generated segmentations of aortic vessel trees from CT imaging. The challenge was organized as a "container submission" challenge, where participants had to upload their algorithms to Grand Challenge in the form of Docker containers. Three tasks were created for SEG.A. 2023. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Digital techniques. |
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General subdivision | Data processing. |
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General subdivision | Data processing. |
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Author 2 | Pepe, Antonio. |
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Author 2 | Melito, Gian Marco. |
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Author 2 | Egger, Jan. |
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Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-53241-2 |
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Koha item type | eBooks |
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-- | Springer Nature Switzerland : |
-- | Imprint: Springer, |
-- | 2024. |
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-- | Image processing |
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-- | Computer vision. |
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-- | Image processing. |
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-- | Social sciences |
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-- | Computers. |
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-- | Education |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
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-- | Image Processing. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Application in Social and Behavioral Sciences. |
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-- | Computing Milieux. |
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-- | Computers and Education. |
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-- | 1611-3349 ; |
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