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Segmentation of the aorta = towards the automatic segmentation, modeling, and meshing of the aortic vessel tree from multicenter acquisition : first challenge, SEG. A. 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Segmentation of the aorta/ edited by Antonio Pepe, Gian Marco Melito, Jan Egger.
其他題名:	towards the automatic segmentation, modeling, and meshing of the aortic vessel tree from multicenter acquisition : first challenge, SEG. A. 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023 : proceedings /
其他題名:	SEG.A 2023
其他作者:	Pepe, Antonio.
團體作者:	SEG.A (Challenge)
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xii, 142 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Aorta - Congresses. - Radiography -
電子資源:	https://doi.org/10.1007/978-3-031-53241-2
ISBN:	9783031532412

Segmentation of the aorta = towards the automatic segmentation, modeling, and meshing of the aortic vessel tree from multicenter acquisition : first challenge, SEG. A. 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023 : proceedings /
Segmentation of the aortatowards the automatic segmentation, modeling, and meshing of the aortic vessel tree from multicenter acquisition : first challenge, SEG. A. 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023 : proceedings /[electronic resource] :SEG.A 2023edited by Antonio Pepe, Gian Marco Melito, Jan Egger. - Cham :Springer Nature Switzerland :2024. - xii, 142 p. :ill., digital ;24 cm. - Lecture notes in computer science,145390302-9743 ;. - Lecture notes in computer science ;14539..

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.

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.

ISBN: 9783031532412

Standard No.: 10.1007/978-3-031-53241-2doiSubjects--Topical Terms:

3712760
Aorta
--Radiography--Congresses.

LC Class. No.: RC691.6.I52

Dewey Class. No.: 616.1380754

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