Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis

Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or Dice, which assume pixels to be independent of each...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 42; H. 2; S. 546 - 556
Hauptverfasser:	Gaggion, Nicolas, Mansilla, Lucas, Mosquera, Candelaria, Milone, Diego H., Ferrante, Enzo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	anatomically plausible segmentation Artificial neural networks Biomedical imaging Chest Coders Computational modeling Convolutional neural networks Decoding Domains Encoders-Decoders Entropy Graph convolutional neural networks graph generative models Graph neural networks Graph representations Graphical representations Image processing Image Processing, Computer-Assisted - methods Image segmentation landmark based segmentation localized skip connections Medical imaging Neural networks Neural Networks, Computer Occlusion Pixels Radiography Shape Thorax - diagnostic imaging X ray analysis X-Rays
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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