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

Abstract Background 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. Methods Here, we tested in simulation whether the foundation mod...

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Uloženo v:
Hlavní autoři: Leo Misera, Sven Nebelung, Zunamys I. Carrero, Keno Bressem, Marta Ligero, Jens-Peter Kühn, Ralf-Thorsten Hoffmann, Daniel Truhn, Jakob Nikolas Kather
Médium: Artigo
Jazyk:Inglês
Vydáno: BMC 2026-01-01
Edice:BMC Medical Imaging
Témata:
Segment anything model
Deep learning
Segmentation
CT
MRI
Image annotation
On-line přístup:https://doi.org/10.1186/s12880-025-02075-4
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