Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmar...

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Detalles Bibliográficos
Autores principales: Stebani, Jannik, Blaimer, Martin, Zabler, Simon, Neun, Tilmann, Pelt, Daniël M., Rak, Kristen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625555/
https://www.ncbi.nlm.nih.gov/pubmed/37925540
http://dx.doi.org/10.1038/s41598-023-45466-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625555/
https://www.ncbi.nlm.nih.gov/pubmed/37925540
http://dx.doi.org/10.1038/s41598-023-45466-9

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