Fetal MRI deep learning segmentation of body and lung in congenital diaphragmatic hernia
DL for body and lung segmentation in fetuses with congenital diaphragmatic hernia allows reliable and rapid calculations of the observed/expected ratio and equally predicts prognostic outcome.
Authors
- Maike Theis (ORCID: https://orcid.org/0000-0003-2666-9928)
- Thomas Vollbrecht (ORCID: https://orcid.org/0000-0002-1162-2839)
- Daniel Kuetting (ORCID: https://orcid.org/0000-0001-6520-1020)
- Julian Alexander Luetkens (ORCID: https://orcid.org/0000-0002-7715-4636)
- Annegret Geipel (ORCID: https://orcid.org/0000-0001-8602-8925)
- Claus Christian Pieper (ORCID: https://orcid.org/0000-0002-0893-1200)
- Florian Kipfmueller (ORCID: https://orcid.org/0000-0002-7414-757X)
- Brigitte Strizek (ORCID: https://orcid.org/0000-0003-3625-7097)
- Sebastian Nowak (ORCID: https://orcid.org/0000-0001-5175-5559)
- Alexander Isaak (ORCID: https://orcid.org/0000-0003-2016-6166)
- Leon Bischoff (ORCID: https://orcid.org/0000-0001-5193-5226)
- Alois M. Sprinkart (ORCID: https://orcid.org/0000-0002-1435-9562)
- Markus Mader (ORCID: https://orcid.org/0009-0005-7733-6965)
Institutions
- University Hospital Bonn (DE)
Publication Details
- Journal
- Radiology Advances
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1093/radadv/umae034
- Citations
- 5
- Primary Topic
- Congenital Diaphragmatic Hernia Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.86