Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation

Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation

In this paper, we propose a dense multi-scale adaptive graph convolutional network (<i>DMA-GCN</i>) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the followin...

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Bibliographic Details
Main Authors: Christos Chadoulos, Dimitrios Tsaopoulos, Andreas Symeonidis, Serafeim Moustakidis, John Theocharis
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Bioengineering
Subjects:
knee cartilage osteoarthritis (<i>KOA</i>)
magnetic resonance imaging (<i>MRI</i>) segmentation
multi-atlas
graph neural networks (<i>GNNs</i>)
deep learning
graph learning
Online Access:https://www.mdpi.com/2306-5354/11/3/278

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