Abstract
Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.
Subject
Health Information Management,Health Informatics,Health Policy,Leadership and Management
Reference60 articles.
1. Rathore, R., and Van Tine, B. (2021). Pathogenesis and Current Treatment of Osteosarcoma: Perspectives for Future Therapies. J. Clin. Med., 10.
2. A review of imaging of surface sarcomas of bone;Skelet. Radiol.,2020
3. Yang, C., Tian, Y., Zhao, F., Chen, Z., Su, P., Li, Y., and Qian, A. (2020). Bone Microenvironment and Osteosarcoma Metastasis. Int. J. Mol. Sci., 21.
4. Large Scale Identification of Osteosarcoma Pathogenic Genes by Multiple Extreme Learning Machine;Front. Cell Dev. Biol.,2021
5. Osteosarcoma: A comprehensive review of management and treatment strategies;Ann. Diagn. Pathol.,2020
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献