VertNet: Accurate Vertebra Localization and Identification Network from CT Images
Zhiming Cui1,2,5, Changjian Li3, Lei Yang2, Chunfeng Lian4, Feng Shi5
Wenping Wang2, Dijia Wu2, Dinggang Shen2
1ShanghaiTech University, 2The University of HongKong, 3University College London
4Xi’an Jiaotong University, 5Shanghai United Imaging Intelligence Co. Ltd.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
Paper teaser
Fig. 1. Typical challenging cases: (a) Spine with pathological fracture; (b) Image with metal artifacts; (c) Adjacent vertebrae having similar shape appearance; (d) Image with a limited field of view.
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.
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Data [Code and Data (coming soon...)]

Cui, Zhiming, Changjian Li, Lei Yang, Chunfeng Lian, Feng Shi, Wenping Wang, Dijia Wu, and Dinggang Shen. "VertNet: Accurate Vertebra Localization and Identification Network from CT Images." In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24, pp. 281-290. Springer International Publishing, 2021.

Algorithm pipeline
Fig. 2. An overview of the proposed VertNet for vertebra localization (Sec. 2.1) and identification (Sec. 2.2) from input CT images.
Representative Results
Fig. 3. Comparison between our results (blue) and those by Deep-HMM (yellow) against the ground-truth (GT) (red). Seven typical examples are presented: metal artifacts(1,7), pathological spines(2,3), and limited field of view (4,5,6,7). The GT label is annotated if incorrect prediction occurs. (Color figure online)
©Changjian Li. Last update: October 21, 2023.