| Autor: |
Ying-shan WANG, Ao-qi DENG, Jin-ling MAO, Zhong-qi ZHU, Jie SHI, Guang YANG, Wei-wei MA, Qing LU, Hong-zhi WANG |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Chinese Journal of Magnetic Resonance, Vol 39, Iss 03, Pp 303-315 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1000-4556 |
| DOI: |
10.11938/cjmr20222988 |
| Popis: |
Knee joint is commonly hurt by rheumatoid arthritis (RA). Accurate segmentation of synovium is essential for the diagnosis and treatment of RA. This paper proposes an algorithm based on improved VNet for automatically segmenting knee joint synovial magnetic resonance images. Firstly, the knee joint magnetic resonance images of 39 patients with synovitis were preprocessed. VNetTrans was constructed by embedding Transformer at the bottom of VNet. The MemSwish activation function was used for training. The average Dice score of the final model is 0.758 5 and the HD is 24.6 mm. Compared with VNet, the proposed model increased Dice score by 0.083 6 and decreased HD by 10 mm. Experimental results demonstrated that the proposed algorithm achieved satisfying 3D segmentation of the synovial hyperplasia area in the knee magnetic resonance images. It can be utilized to facilitate the diagnosis and monitoring of RA. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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