The potential for different computed tomography-based machine learning networks to automatically segment and differentiate pelvic and sacral osteosarcoma from Ewing’s sarcoma

BACKGROUND: This study aimed to explore optimal computed tomography (CT)-based machine learning and deep learning methods for the identification of pelvic and sacral osteosarcomas (OS) and Ewing’s sarcomas (ES). METHODS: A total of 185 patients with pathologically confirmed pelvic and sacral OS and...

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Detalles Bibliográficos
Autores principales: Yin, Ping, Wang, Wenjia, Wang, Sicong, Liu, Tao, Sun, Chao, Liu, Xia, Chen, Lei, Hong, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167450/
https://www.ncbi.nlm.nih.gov/pubmed/37179923
http://dx.doi.org/10.21037/qims-22-1042

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167450/
https://www.ncbi.nlm.nih.gov/pubmed/37179923
http://dx.doi.org/10.21037/qims-22-1042

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