VertNet: Accurate Vertebra Localization and Identification Network from CT Images

Accurate localization and identification of vertebrae from CT images is a fundamental step in clinical spine diagnosis and treatment. Previous methods have made various attempts in this task; however, they fail to robustly localize the vertebrae with challenging appearance or identify vertebra labels from CT images with a limited field of view. In this paper, we propose a novel two-stage framework, VertNet, for accurate and robust vertebra localization and identification from CT images. Our method first detects all vertebra centers by a weighted voting-based localization network. Then, an identification network is designed to identify the label of each detected vertebra in leveraging the synergy of global and local information. Specifically, a bidirectional relation module is designed to learn the global correlation among vertebrae along the upward and downward directions, and a continuous label map with dense annotation is employed to enhance the feature learning in local vertebra patches. Extensive experiments on a large dataset collected from real-world clinics show that our framework can accurately localize and identify vertebrae in various challenging cases and outperforms the state-of-the-art methods.