Name
Abstract | ||
|---|---|---|
Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22093258 | SENSORS |
Keywords | DocType | Volume |
scoliosis, Cobb angle measurement, deep learning, image enhancement | Journal | 22 |
Issue | ISSN | Citations |
9 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Jin | 1 | 0 | 0.34 |
| Shengru Wang | 2 | 0 | 0.34 |
| Guodong Yang | 3 | 1 | 2.32 |
| En Li | 4 | 0 | 1.35 |
| Zize Liang | 5 | 0 | 0.34 |