Your browser doesn't support javascript.
loading
Quality Assessment of Brain MRI Defacing Using Machine Learning.
Sadeghi, Sina; Khodaei, Maryam; Hempel, Lars; Kirsten, Toralf.
Afiliación
  • Sadeghi S; Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
  • Khodaei M; Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
  • Hempel L; Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
  • Kirsten T; Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
Stud Health Technol Inform ; 316: 636-637, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39176821
ABSTRACT
Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Imagen por Resonancia Magnética / Aprendizaje Automático Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Imagen por Resonancia Magnética / Aprendizaje Automático Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos