Title 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 / Antonio Pepe, Gian Marco Melito, Jan Egger, editors

Published Cham : Springer, 2024

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Description 1 online resource (154 p.) Series Lecture Notes in Computer Science ; 14539 Lecture notes in computer science ; 14539. Contents Intro -- Preface -- Organization -- Contents -- M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 Image Preprocessing -- 2.3 M3F: Multi-Field-of-View Feature Fusion Network -- 2.4 Network Training -- 2.5 Sliding Window Inference -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Results -- 4 Discussion -- References -- Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge -- 1 Introduction -- 2 Methods -- 2.1 SegResNet -- 2.2 Optimization -- 3 Results 4 Conclusion -- References -- A Data-Centric Approach for Segmenting the Aortic Vessel Tree: A Solution to SEG.A. Challenge 2023 Segmentation Task -- 1 Introduction -- 1.1 Segmentation of the Aorta Challenge 2023 -- 1.2 Related Work -- 2 Methods -- 2.1 Challenge Dataset -- 2.2 System Architecture -- 2.3 Preprocessing -- 2.4 Augmentations -- 2.5 Training -- 2.6 Postprocessing -- 2.7 Inference -- 2.8 Assessment Methods -- 3 Results -- 3.1 Quantitative Analysis -- 3.2 Qualitative Analysis -- 3.3 Challenge Submission -- 3.4 Selected Teams -- 4 Conclusion -- References Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge -- 1 Introduction -- 1.1 Overview -- 1.2 Related Work -- 1.3 Design Choices -- 1.4 Contribution -- 2 Method -- 2.1 Preprocessing -- 2.2 Augmentation -- 2.3 Deep Network -- 2.4 Objective Function and Training -- 2.5 Inference and Postprocessing -- 2.6 Meshing -- 2.7 Dataset -- 2.8 Experimental Setup -- 2.9 Source Code -- 3 Results -- 3.1 Aorta Segmentation -- 3.2 Surface Meshing -- 3.3 Volumetric Meshing -- 4 Discussion -- References Position-Encoded Pixel-to-Prototype Contrastive Learning for Aortic Vessel Tree Segmentation -- 1 Introduction -- 2 Problem Definition -- 3 Methods -- 3.1 Framework -- 3.2 First Stage: Coarse Segmentation -- 3.3 Second Stage: Fine Segmentation -- 4 Experiment and Results -- 4.1 Setup -- 4.2 Results -- 4.3 Leaderboard -- 5 Discussion -- 6 Conclusion -- References -- Misclassification Loss for Segmentation of the Aortic Vessel Tree -- 1 Introduction -- 2 Methods -- 2.1 nnUNet Architecture -- 2.2 Misclassification Loss -- 3 Experiments -- 3.1 Ablation Studies -- 3.2 Results and Discussions 4 Limitations -- 5 Conclusion -- References -- Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Preprocessing -- 3.2 Segmentation Method -- 3.3 Mesh Construction Method -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Evaluation Scheme and Metrics -- 4.3 Ablation Study -- 4.4 Learning Curves -- 4.5 Qualitative Results -- 4.6 Quantitative Results -- 4.7 Interpretation in Mesh Quality and Plots -- 5 Conclusion -- References Summary 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 Notes RASNet: U-Net-Based Robust Aortic Segmentation Network for Multicenter Datasets Online resource; title from PDF title page (Springerlink, viewed February 22, 2024) Subject Aorta -- Radiography -- Congresses Diagnostic imaging -- Data processing -- Congresses Form Electronic book Author Pepe, Antonio Melito, Gian Marco Egger, Jan International Conference on Medical Image Computing and Computer-Assisted Intervention (26th : 2023 : Vancouver, B.C.) ISBN 9783031532412 3031532414

Other Titles SEG.A 2023

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