Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

SIMPLE SUMMARY: Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with a self-configurable nnU-Net for fully automated tumor segmentation on serially acquired dynamic contrast-enhanced MRI i...

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Detalles Bibliográficos
Autores principales: Xu, Zhan, Rauch, David E., Mohamed, Rania M., Pashapoor, Sanaz, Zhou, Zijian, Panthi, Bikash, Son, Jong Bum, Hwang, Ken-Pin, Musall, Benjamin C., Adrada, Beatriz E., Candelaria, Rosalind P., Leung, Jessica W. T., Le-Petross, Huong T. C., Lane, Deanna L., Perez, Frances, White, Jason, Clayborn, Alyson, Reed, Brandy, Chen, Huiqin, Sun, Jia, Wei, Peng, Thompson, Alastair, Korkut, Anil, Huo, Lei, Hunt, Kelly K., Litton, Jennifer K., Valero, Vicente, Tripathy, Debu, Yang, Wei, Yam, Clinton, Ma, Jingfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571741/
https://www.ncbi.nlm.nih.gov/pubmed/37835523
http://dx.doi.org/10.3390/cancers15194829

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571741/
https://www.ncbi.nlm.nih.gov/pubmed/37835523
http://dx.doi.org/10.3390/cancers15194829

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