Baumgartner Christian F, Kamnitsas Konstantinos, Matthew Jacqueline, Fletcher Tara P, Smith Sandra, Koch Lisa M, Kainz Bernhard, Rueckert Daniel
IEEE Trans Med Imaging. 2017 Nov;36(11):2204-2215. doi: 10.1109/TMI.2017.2712367. Epub 2017 Jul 11.
Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.
在二维超声孕中期检查中识别和解读胎儿标准扫描平面是非常复杂的任务，需要多年的训练。除了将探头引导到正确位置外，对于非专业人员来说，在图像中识别相关结构同样困难。自动图像处理可以提供工具，帮助有经验和无经验的操作员完成这些任务。在本文中，我们提出了一种基于卷积神经网络的新方法，该方法可以自动检测徒手二维超声数据中的13个胎儿标准视图，并通过边界框提供胎儿结构的定位。一个重要的贡献是，该网络仅基于图像级标签使用弱监督来学习定位目标解剖结构。网络架构设计为实时运行，同时为定位任务提供最佳输出。我们展示了实时标注、从保存的视频中进行回顾性帧检索以及在一个由完整临床异常筛查的图像和视频记录组成的非常大且具有挑战性的数据集上进行定位的结果。我们发现，在模拟实时检测的实际分类实验中，所提出的方法平均F1分数达到0.798，回顾性帧检索的准确率为9…显示全部
在二维超声孕中期检查中识别和解读胎儿标准扫描平面是非常复杂的任务，需要多年的训练。除了将探头引导到正确位置外，对于非专业人员来说，在图像中识别相关结构同样困难。自动图像处理可以提供工具，帮助有经验和无经验的操作员完成这些任务。在本文中，我们提出了一种基于卷积神经网络的新方法，该方法可以自动检测徒手二维超声数据中的13个胎儿标准视图，并通过边界框提供胎儿结构的定位。一个重要的贡献是，该网络仅基于图像级标签使用弱监督来学习定位目标解剖结构。网络架构设计为实时运行，同时为定位任务提供最佳输出。我们展示了实时标注、从保存的视频中进行回顾性帧检索以及在一个由完整临床异常筛查的图像和视频记录组成的非常大且具有挑战性的数据集上进行定位的结果。我们发现，在模拟实时检测的实际分类实验中，所提出的方法平均F1分数达到0.798，回顾性帧检索的准确率为90.09%。此外，定位任务的准确率达到77.8%。
