Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images
文献类型：期刊论文
| 作者 | Zeng, Yu3,4; Wang, Kun3,4 ; Dai, Lai4; Wang, Changqing3; Xiong, Chi2,4; Xiao, Peng1,4; Cai, Bin4; Zhang, Qiang4; Sun, Zhiyong4; Cheng, Erkang4
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| 刊名 | ELECTRONICS
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| 出版日期 | 2024-06-01 |
| 卷号 | 13 |
| 关键词 | lumbar vertebrae detection spatial relationship deep learning X-ray image analysis |
| DOI | 10.3390/electronics13112137 |
| 通讯作者 | Cheng, Erkang(ekcheng@iim.ac.cn) ; Song, Bo(songbo@iim.ac.cn) |
| 英文摘要 | Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively. |
| WOS关键词 | RECOGNITION |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Computer Science ; Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001246711300001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136172]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Cheng, Erkang; Song, Bo |
| 作者单位 | 1.Hefei Univ, Sch Artificial Intelligence & Big Data, Hefei 230601, Peoples R China 2.Univ Sci & Technol China, Dept Life Sci & Med, Hefei 230026, Peoples R China 3.Anhui Med Univ, Sch Biomed Engn, Hefei 230032, Peoples R China 4.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zeng, Yu,Wang, Kun,Dai, Lai,et al. Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images[J]. ELECTRONICS,2024,13. |
| APA | Zeng, Yu.,Wang, Kun.,Dai, Lai.,Wang, Changqing.,Xiong, Chi.,...&Song, Bo.(2024).Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images.ELECTRONICS,13. |
| MLA | Zeng, Yu,et al."Exploring Neighbor Spatial Relationships for Enhanced Lumbar Vertebrae Detection in X-ray Images".ELECTRONICS 13(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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