论文标题
使用不同类型的注释的小肠路径跟踪的深度加固学习
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations
论文作者
论文摘要
考虑到它的许多折叠并沿着路线接触，小肠路径跟踪是一个具有挑战性的问题。出于同样的原因，在3D中实现小肠的地面真相（GT）路径非常昂贵。在这项工作中，我们建议使用具有不同类型的注释的数据集训练深入的增强学习跟踪器。具体而言，我们利用只有GT小肠分割的CT扫描以及带有GT路径的CT扫描。它可以通过设计一个兼容两者兼容的独特环境来启用，即使没有GT路径，也可以定义奖励。进行的实验证明了该方法的有效性。提出的方法通过能够使用弱注释来利用扫描，从而可以通过降低所需的注释成本，从而在此问题中具有高度的可用性。
Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT) path of the small bowel in 3D. In this work, we propose to train a deep reinforcement learning tracker using datasets with different types of annotations. Specifically, we utilize CT scans that have only GT small bowel segmentation as well as ones with the GT path. It is enabled by designing a unique environment that is compatible for both, including a reward definable even without the GT path. The performed experiments proved the validity of the proposed method. The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.
