Extraction of Airways using Graph Neural Networks
This work addresses airway extraction from medical images, which is important for clinical diagnosis and analysis, but appears incremental as it builds on existing graph-based methods.
The paper tackled the problem of extracting tree structures like airways from 3D chest CT scans by framing it as a graph refinement task, proposing a graph auto-encoder model with a GNN-based encoder and decoder to predict node connections, and compared its performance with mean-field networks.
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.