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Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2

Volumetric segmentation in CT and MRI is valuable for artificial intelligence workflows in radiology, yet creating the large, precisely annotated datasets required for training segmentation models remains laborious.

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Detalles Bibliográficos
Autores principales: Misera, Leo (Autor) , Nebelung, Sven (Autor) , Carrero, Zunamys I. (Autor) , Bressem, Keno (Autor) , Ligero, Marta (Autor) , Kühn, Jens-Peter (Autor) , Hoffmann, Ralf-Thorsten (Autor) , Truhn, Daniel (Autor) , Kather, Jakob Nikolas (Autor)
Formato: Article (Journal)
Lenguaje:inglés
Publicado: 2026
In: BMC medical imaging
Year: 2026, Volumen: 26, Pages: 1-12
ISSN:1471-2342
DOI:10.1186/s12880-025-02075-4
Acceso en línea:Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12880-025-02075-4
Enlace del recurso
Notas de Autor:Leo Misera, Sven Nebelung, Zunamys I. Carrero, Keno Bressem, Marta Ligero, Jens-Peter Kühn, Ralf-Thorsten Hoffmann, Daniel Truhn and Jakob Nikolas Kather
Descripción
Sumario:Volumetric segmentation in CT and MRI is valuable for artificial intelligence workflows in radiology, yet creating the large, precisely annotated datasets required for training segmentation models remains laborious.
Notas:Online veröffentlicht: 8. Januar 2026
Gesehen am 27.05.2026
Descripción Física:Online Resource
ISSN:1471-2342
DOI:10.1186/s12880-025-02075-4

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