Multi-class Segmentation of the Aorta

AortaSeg 2024 Challenge, Held in Conjunction with MICCAI 2024, Virtual Event, October 24, 2024, Proceedings
Erscheinungsjahr 2026
ISBN: 978-3-032-14245-0
Verlag: Springer

Buch, Englisch, 123 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the First MICCAI Challenge Multi-class Segmentation of the Aorta, AortaSeg 2024, held in conjunction with MICCAI 2024, as a virtual event, during October 2024. 

The 10 papers included in the book were carefully reviewed and selected from 16 submitting teams. This challenge aimed to advance the field of medical image segmentation by introducing the first large-scale, publicly available dataset for multi-class segmentation of the aorta, its branches, and clinically relevant zones in computed tomography angiography (CTA).

Zielgruppe

Research

Autoren/Hrsg.

Imran, Muhammad

Krebs, Jonathan R.

Cooper, Michol A.

Ma, Jun

Zhou, Yuyin

Shao, Wei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik

Inhaltsverzeichnis

.- Multi-Class Segmentation of Aortic Branches and Zones in CTA

.- Coarse-to-Fine Aortic Segmentation on CTA Using a Two-Stage nnUNet-Based Framework.
.- Hierarchical Semantic Learning for Multi-Class Aorta Segmentation.
.- U-Net-Based Segmentation of Aortic Branches and Zones in CTA Scans.
.- Anatomically Guided Two-Stage 3D Aorta Segmentation in CT Angiography.
.- Combining Region-Based and Topological Losses in the nnU-Net Framework for Advanced Aorta Segmentation.
.- Data-Centric Multiclass Aortic Segmentation: Revisiting Classical Architectures in Low-Data Regimes.
.- AortaST: A Student-Teacher Framework for Multi-Class Aortic Segmentation.
.- Accurate and Efficient Multi-Class Segmentation for Aortic Branches and Zones in CTA.
.- Application of nnUNet for Multi-Class Segmentation of Aortic Branches and Zones in CTA.
.- A Mamba-Based Method with Gated Attention for Human Aorta Segmentation.