Morphology-Aware Interactive Keypoint Estimation
This addresses the bottleneck of human effort in medical image diagnosis by providing an interactive tool for doctors, though it is incremental as it builds on existing deep-learning methods.
The paper tackles the problem of manual annotation of anatomical keypoints in X-ray images by proposing a deep neural network that automates detection and allows doctors to refine predictions interactively with fewer clicks, reducing annotation costs as demonstrated on collected and public datasets.
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.