Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification
This study proposed a text-guided DL method for segmenting spinal structures and identifying lumbar abnormalities in multi-sequence MRI scans. By integrating cross-modal features, our method improved segmentation accuracy and optimized identification performance, demonstrating clinical potential to assist diagnosis through automated workflows that reduce radiologist workload.
Authors
- Xiaopeng Guo (ORCID: https://orcid.org/0000-0002-4183-5560)
- Zhangzhen Shi (ORCID: https://orcid.org/0000-0001-6550-1509)
- Xinran Zhu (ORCID: https://orcid.org/0000-0001-9950-3959)
- Jingxin Liu (ORCID: https://orcid.org/0000-0001-6071-9197)
- Kailiang Cheng (ORCID: https://orcid.org/0000-0003-0092-4679)
- Donghong An
- Lihui Zu
Institutions
- Union Hospital (HK)
- Jilin University (CN)
- Union Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-09-24
- DOI
- https://doi.org/10.21037/qims-2025-635
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00