基于深度学习的三阴性乳腺癌动态对比增强MRI序列图像全自动肿瘤分割
Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.
作者信息
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
机构信息
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
出版信息
Cancers (Basel). 2023 Oct 2;15(19):4829. doi: 10.3390/cancers15194829.
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.
准确的肿瘤分割是定量图像分析所必需的，而定量图像分析越来越多地用于肿瘤评估。我们使用一个可自我配置的深度学习框架和大量在患者治疗过程中连续采集的动态对比增强MRI图像，开发了一种三阴性乳腺癌的全自动高性能分割模型。在所有模型中，使用治疗过程中不同时间点的图像训练的表现最佳的模型在基线图像上的Dice相似系数为93%，灵敏度为96%。表现最佳的模型还能产生准确的肿瘤大小测量结果，这对实际临床应用很有价值。
Zotero 插件