论文标题
随着时间的推移解决3D超声检查的表示形式学习技术的比较
Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound
论文作者
论文摘要
3D超声（3DU）对于辐射疗法的靶标跟踪变得更加有趣，因为它的能力无需使用电离辐射就可以实时提供体积图像。它可能无需使用基金会而用于跟踪。为此，一种学习有意义表示的方法对于识别表示空间（R空间）中不同时间范围的解剖结构将是有用的。在这项研究中，使用常规自动编码器，变分自动编码器和切成薄片 - 韦森斯坦自动编码器将3DUS斑块简化为128维的R空间。在R空间中，研究并根据肝图像数据集进行了研究并比较分离不同的超声贴片以及识别类似斑块的能力。提出了两个评估R空间跟踪功能的指标。结果表明，可以区分具有不同解剖结构的超声贴片，并且可以将类似贴片的集合聚类为R空间。结果表明，研究的自动编码器在3DU中具有不同级别的可用性。
3D ultrasound (3DUS) becomes more interesting for target tracking in radiation therapy due to its capability to provide volumetric images in real-time without using ionizing radiation. It is potentially usable for tracking without using fiducials. For this, a method for learning meaningful representations would be useful to recognize anatomical structures in different time frames in representation space (r-space). In this study, 3DUS patches are reduced into a 128-dimensional r-space using conventional autoencoder, variational autoencoder and sliced-wasserstein autoencoder. In the r-space, the capability of separating different ultrasound patches as well as recognizing similar patches is investigated and compared based on a dataset of liver images. Two metrics to evaluate the tracking capability in the r-space are proposed. It is shown that ultrasound patches with different anatomical structures can be distinguished and sets of similar patches can be clustered in r-space. The results indicate that the investigated autoencoders have different levels of usability for target tracking in 3DUS.
