Siemens Healthineers, Medical Imaging Technologies, Princeton, NJ, USA; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Siemens Healthineers, Medical Imaging Technologies, Princeton, NJ, USA.
Med Image Anal. 2018 Aug;48:203-213. doi: 10.1016/j.media.2018.06.007. Epub 2018 Jun 23.
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi-scale image analysis, artificial agents are taught optimal navigation paths in the scale-space representation of an image, while accounting for structures that are missing from the field-of-view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D-CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false-positive and 0% false-negative rates at detecting whether the landmarks are captured in the field-of-view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection-time of the marginal space deep learning method by 20-30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D-CT data.
稳健且快速的解剖结构检测是医学图像分析技术的重要组成部分。当前的解剖结构检测解决方案基于机器学习，并且通常受到次优和穷尽搜索策略的驱动。特别是，这些技术不能有效地解决数据不完整的情况，即，使用部分视野获取的扫描。我们通过遵循一种新的范例来应对这些挑战，该范例将检测任务重新表述为教导智能人工智能代理如何主动搜索解剖结构。使用具有多尺度图像分析的深度强化学习原理，代理被教导在图像的尺度空间表示中进行最佳导航路径，同时考虑到视野中缺失的结构。使用统计形状建模和稳健估计理论的元素来确保观察到的解剖学标记的空间一致性。实验表明，我们的解决方案在不出现任何故障的情况下，在检测不同的解剖结构方面优于边缘空间深度学习，这是一种强大的深度学习方法。该数据集包含来自 2000 多名患者的 5043 个 3D-CT 容积，总共有超过 200 万张图像切片。特别是，我们的解决方案在检测标记是否被扫描的视野捕获方面（不包括所有边界情况）实现了 0%的假阳性率和 0%的假阴性率，平均检测准确率为 2.78mm。在运行时方面，我们将边缘空间深度学习方法的检测时间减少了 20-30 倍，降至 40ms 以下，这是针对高分辨率不完整 3D-CT 数据的无与伦比的性能。
