  Diagnosis based on medical images, such as X-ray images, often involves
manual annotation of anatomical keypoints. However, this process involves
significant human efforts and can thus be a bottleneck in the diagnostic
process. To fully automate this procedure, deep-learning-based methods have
been widely proposed and have achieved high performance in detecting keypoints
in medical images. However, these methods still have clinical limitations:
accuracy cannot be guaranteed for all cases, and it is necessary for doctors to
double-check all predictions of models. In response, we propose a novel deep
neural network that, given an X-ray image, automatically detects and refines
the anatomical keypoints through a user-interactive system in which doctors can
fix mispredicted keypoints with fewer clicks than needed during manual
revision. Using our own collected data and the publicly available AASCE
dataset, we demonstrate the effectiveness of the proposed method in reducing
the annotation costs via extensive quantitative and qualitative results. A demo
video of our approach is available on our project webpage.
