用条件生成对抗网络进行图像推算,以计算机断层扫描上捕获临床相关的成像特征.
Joseph Rich1,2, Jonathan Le1, Ragheb Raad3
1Department of Radiology, University of Southern California, Los Angeles, California, United States of America.
有条件的生成对抗网络 (cGAN) 可以成功地归咎于缺失的癌CT图像. 这些生成的图像保留了临床相关的特征,有助于诊断和治疗规划.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 癌是一种常见的成人恶性瘤,通常使用四相计算机断层扫描 (CT) 成像来诊断.
- 缺失或损坏的CT图像在癌检测,诊断和治疗规划方面构成了重大挑战.
- 深度学习,特别是条件生成对抗网络 (cGANs),显示出对归因失踪的医学成像数据的承诺.
研究的目的:
- 探索cGANs用于四相癌CT研究的图像的临床实用性.
- 评估cGAN输入图像在保留临床相关诊断信息方面的准确性和可靠性.
主要方法:
- 对333名患者数据集进行了cGAN的训练,以根据其他三个数据集计算一个缺失的CT阶段.
- 在对37名患者的单独测试组上评估了cGAN的归算性能.
- 从临床上相关的成像特征 (共21个,包括13个分类特征) 从真实和假设图像中手动提取,以进行比较.
主要成果:
- 假设图像显示了所有13个分类临床特征与真实图像的高度一致 (超过85%的一致).
- 这种准确性在不同的成像阶段是一致的.
- 假定图像在定量放射性特征 (如平均强度和增强) 上显示出很好的一致性,并保持了良性或恶性分类的诊断特征.
结论:
- 通过cGAN输入的图像有效地保留了四相癌CT扫描中的基本定性和定量特征.
- 高精度和特征保留表明cGAN输入图像的临床应用具有重大潜力.
- 这项技术可以帮助克服医学成像中缺失或损坏数据所带来的挑战,改善患者护理.
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