Extraction of Airways using Graph Neural Networks
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Abstract (arXiv)
We present extraction of tree structures, such as airways, from image data as a graph refinement task. To this end, we propose a graph auto-encoder model that uses an encoder based on graph neural networks (GNNs) to learn embeddings from input node features and a decoder to predict connections between nodes. Performance of the GNN model is compared with mean-field networks in their ability to extract airways from 3D chest CT scans.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.
- 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
- 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
- 2023Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentationcited by 9
- 2023Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation10 Mar 2023
- 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective27 Sep 2022
- 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future27 May 2021