一种鲁棒高效的基于时空框架的AI助手，用于从DCE-MRI图像中进行乳腺肿瘤分割。
A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.
发表日期：2023 Sep 08
作者：
Jiadong Zhang, Zhiming Cui, Zhenwei Shi, Yingjia Jiang, Zhiliang Zhang, Xiaoting Dai, Zhenlu Yang, Yuning Gu, Lei Zhou, Chu Han, Xiaomei Huang, Chenglu Ke, Suyun Li, Zeyan Xu, Fei Gao, Luping Zhou, Rongpin Wang, Jun Liu, Jiayin Zhang, Zhongxiang Ding, Kun Sun, Zhenhui Li, Zaiyi Liu, Dinggang Shen
来源：
CLINICAL PHARMACOLOGY & THERAPEUTICS
摘要：
动态对比增强磁共振成像（DCE-MRI）具有高敏感性，可用于乳腺肿瘤的筛查、随访和诊断。从DCE-MRI中准确地分割肿瘤可以提供关键的肿瘤位置和形状信息，这对下游的临床决策有重要影响。在本文中，我们旨在开发一种人工智能（AI）助手，通过捕捉多相DCE-MRI中的动态变化，用空间-时间框架自动分割乳腺肿瘤。我们的AI助手的主要优势包括（1）鲁棒性，即我们的模型能处理具有不同相数和成像间隔的MR数据，在来自七个医疗中心的大规模数据集上得到验证，以及（2）高效性，即我们的AI助手能够将手动标注所需的时间缩短了20倍，同时保持与医生相当的准确性。更重要的是，作为构建AI辅助乳腺癌诊断系统的基本步骤，我们的AI助手将促进AI在乳腺癌相关的临床诊断实践中的应用。© 2023作者。
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.© 2023 The Authors.