Automated segmentation of lung, liver, and liver tumors from Tc‐99m MAA SPECT/CT images for Y‐90 radioembolization using convolutional neural networks

PURPOSE: (90)Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ‐at‐risks is labor‐intensive and time‐consuming in (90)Y SIRT planning. In this study, we developed a convolutional neural network (C...

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Detalles Bibliográficos
Autores principales: Chaichana, Anucha, Frey, Eric C., Teyateeti, Ajalaya, Rhoongsittichai, Kijja, Tocharoenchai, Chiraporn, Pusuwan, Pawana, Jangpatarapongsa, Kulachart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
QUANTITATIVE IMAGING AND IMAGE PROCESSING
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298038/
https://www.ncbi.nlm.nih.gov/pubmed/34657293
http://dx.doi.org/10.1002/mp.15303

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298038/
https://www.ncbi.nlm.nih.gov/pubmed/34657293
http://dx.doi.org/10.1002/mp.15303

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