Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability

OBJECTIVES: To develop an automatic method for accurate and robust thalamus segmentation in T1w-MRI for widespread clinical use without the need for strict harmonization of acquisition protocols and/or scanner-specific normal databases. METHODS: A three-dimensional convolutional neural network (3D-C...

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Detalles Bibliográficos
Autores principales: Opfer, Roland, Krüger, Julia, Spies, Lothar, Ostwaldt, Ann-Christin, Kitzler, Hagen H., Schippling, Sven, Buchert, Ralph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935653/
https://www.ncbi.nlm.nih.gov/pubmed/36264314
http://dx.doi.org/10.1007/s00330-022-09170-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935653/
https://www.ncbi.nlm.nih.gov/pubmed/36264314
http://dx.doi.org/10.1007/s00330-022-09170-y

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