A multi-stage semi-supervised learning for ankle fracture classification on CT images
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917625344294912 |
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| author | Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang |
| author_facet | Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang |
| contents | Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19983 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A multi-stage semi-supervised learning for ankle fracture classification on CT images Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang Image and Video Processing Computer Vision and Pattern Recognition Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes. |
| title | A multi-stage semi-supervised learning for ankle fracture classification on CT images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.19983 |