학술논문
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer
Document Type
article
Author
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
Source
Cancers, Vol 15, Iss 19, p 4829 (2023)
Subject
Language
English
ISSN
2072-6694
Abstract
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 treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.