Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.1007/978-3-030-87240-3_27 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V |
DocType | Volume | ISSN |
Conference | 12905 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiming Cui | 1 | 4 | 6.48 |
Chang-Jian Li | 2 | 36 | 4.23 |
Lei Yang | 3 | 12 | 2.87 |
Chunfeng Lian | 4 | 132 | 22.61 |
Feng Shi | 5 | 344 | 32.30 |
Wenping Wang | 6 | 2491 | 176.19 |
Dijia Wu | 7 | 102 | 13.75 |
Dinggang Shen | 8 | 7837 | 611.27 |