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

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

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of...

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Bibliographic Details
Main Authors: Zhan Xu, David E. Rauch, Rania M. Mohamed, Sanaz Pashapoor, Zijian Zhou, Bikash Panthi, Jong Bum Son, Ken-Pin Hwang, Benjamin C. Musall, Beatriz E. Adrada, Rosalind P. Candelaria, Jessica W. T. Leung, Huong T. C. Le-Petross, Deanna L. Lane, Frances Perez, Jason White, Alyson Clayborn, Brandy Reed, Huiqin Chen, Jia Sun, Peng Wei, Alastair Thompson, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Wei Yang, Clinton Yam, Jingfei Ma
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Cancers
Subjects:
deep learning
tumor segmentation
triple-negative breast cancer
Online Access:https://www.mdpi.com/2072-6694/15/19/4829

Internet

https://www.mdpi.com/2072-6694/15/19/4829

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