Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.
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ABSTRACT:
SUBMITTER: Xu Z
PROVIDER: S-EPMC10571741 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
Publications
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.
Xu Zhan Z Rauch David E DE Mohamed Rania M RM Pashapoor Sanaz S Zhou Zijian Z Panthi Bikash B Son Jong Bum JB Hwang Ken-Pin KP Musall Benjamin C BC Adrada Beatriz E BE Candelaria Rosalind P RP Leung Jessica W T JWT Le-Petross Huong T C HTC Lane Deanna L DL Perez Frances F White Jason J Clayborn Alyson A Reed Brandy B Chen Huiqin H Sun Jia J Wei Peng P Thompson Alastair A Korkut Anil A Huo Lei L Hunt Kelly K KK Litton Jennifer K JK Valero Vicente V Tripathy Debu D Yang Wei W Yam Clinton C Ma Jingfei J
Cancers 20231002 19
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 dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a ...[more]
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