Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation

OBJECTIVES: To develop and test a Retina U-Net algorithm for the detection of primary lung tumors and associated metastases of all stages on FDG-PET/CT. METHODS: A data set consisting of 364 FDG-PET/CTs of patients with histologically confirmed lung cancer was used for algorithm development and inte...

Descripción completa

Detalles Bibliográficos
Autores principales: Weikert, T., Jaeger, P. F., Yang, S., Baumgartner, M., Breit, H. C., Winkel, D. J., Sommer, G., Stieltjes, B., Thaiss, W., Bremerich, J., Maier-Hein, K. H., Sauter, A. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182147/
https://www.ncbi.nlm.nih.gov/pubmed/36625882
http://dx.doi.org/10.1007/s00330-022-09332-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182147/
https://www.ncbi.nlm.nih.gov/pubmed/36625882
http://dx.doi.org/10.1007/s00330-022-09332-y

Ejemplares similares