Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net
The deep learning DCU-Net algorithm model can improve the performance of tumor CT image segmentation, and the reconstructed fine model can better reflect the actual situation of individual tumors; the MR system constructed based on this model enhances clinicians' understanding of tumor morphology and spatial relationships. The MR system based on deep learning and three-dimensional visualization technology has great potential in the diagnosis and treatment of bone tumors, and is expected to promote clinical practice and improve efficacy.
Authors
- Jianlong Huang (ORCID: https://orcid.org/0000-0001-7224-4950)
- Yong Chol Han (ORCID: https://orcid.org/0000-0003-0990-9184)
- Daxin Zhu (ORCID: https://orcid.org/0000-0002-8060-7368)
- Yifeng Huang (ORCID: https://orcid.org/0000-0002-0412-5279)
- Kun Wang (ORCID: https://orcid.org/0000-0003-0316-0520)
- Yijie Chen (ORCID: https://orcid.org/0000-0003-4791-9762)
- Yuguang Ye (ORCID: https://orcid.org/0000-0002-1059-5664)
- Yusi Chen
- Ying Huang (ORCID: https://orcid.org/0009-0007-1749-1773)
- Bijiao Ding
- Jianshe Shi
Institutions
- Huaqiao University (CN)
- Quanzhou Normal University (CN)
Publication Details
- Journal
- Journal of bone oncology
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1016/j.jbo.2024.100654
- Citations
- 6
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Fujian Province
- Huaqiao University
- Fujian Provincial Big Data Research Institute of Intelligent Manufacturing
- Science and Technology Bureau of Quanzhou