Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes

Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging issue, especially when there are long-distance objects with close semantic relations and neighboring objects with indistinct boundaries. We prop...

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Veröffentlicht in:	Knowledge-based systems Jg. 236; S. 107360
Hauptverfasser:	Xuan, Ping, Cui, Hui, Zhang, Hongda, Zhang, Tiangang, Wang, Linlin, Nakaguchi, Toshiya, Duh, Henry B.L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 25.01.2022 Elsevier Science Ltd
Schlagworte:	Ablation Attention Attributes Boundaries Computed tomography Computerization Dynamic graph convolutional autoencoder Extraction Graph node attributes Image segmentation Inference Innovations Kidney and tumor segmentation Kidneys Long-distance relationship between nodes Medical imaging Node-attribute-wise attention Nodes Segmentation Semantic relations Semantics Tomography Topology Tumors Node-attribute-wise attention Kidney and tumor segmentation Long-distance relationship between nodes Graph node attributes Dynamic graph convolutional autoencoder
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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