Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images

PURPOSE: To improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN). MATERIALS AND METHODS: From 233 healthy volunteers and 100 patients, 1891 coronal MR images were acquired....

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Detalles Bibliográficos
Autores principales: Crisosto, Cristian, Voskrebenzev, Andreas, Gutberlet, Marcel, Klimeš, Filip, Kaireit, Till F., Pöhler, Gesa, Moher, Tawfik, Behrendt, Lea, Müller, Robin, Zubke, Maximilian, Wacker, Frank, Vogel-Claussen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168553/
https://www.ncbi.nlm.nih.gov/pubmed/37159468
http://dx.doi.org/10.1371/journal.pone.0285378

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168553/
https://www.ncbi.nlm.nih.gov/pubmed/37159468
http://dx.doi.org/10.1371/journal.pone.0285378

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