文章：

深度学习在三阴性乳腺癌连续动态对比增强磁共振成像图像中的全自动肿瘤分割应用

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

原文发布日期：2 October 2023

DOI: 10.3390/cancers15194829

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

定量图像分析日益成为肿瘤评估的重要手段，其前提是精确的肿瘤分割。本研究基于自配置深度学习框架，利用患者在治疗过程中连续采集的大量动态对比增强磁共振图像，开发了一种全自动、高性能的三阴性乳腺癌分割模型。在所有模型中，采用治疗全程多时间点图像进行训练的优化模型在基线图像上实现了93%的戴斯相似系数和96%的灵敏度。该模型还能生成精确的肿瘤尺寸测量结果，这对临床实际应用具有重要价值。

原文链接：

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