| Autor: |
Kuang X, Cheung JP, Huang T, Zhang T |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2023 Jul; Vol. 2023, pp. 1-4. |
| DOI: |
10.1109/EMBC40787.2023.10485565 |
| Abstrakt: |
Most lumbar quantitative assessment methods can only analyze the image from one view and require laborious manual annotation. We aim to develop an unsupervised pipeline for 3D quantitative assessment of the lumbar spine that can assess the MRI with different views. We combine rule-based and deep learning methods to generate multi-tissue segmentation, and parameters can be measured from segmentation results using the anatomical and geometric prior. Preliminary testing demonstrates that our proposed method can generate accurate segmentation and measurement results.Clinical Relevance- The proposed unsupervised 3D lumbar quantitative assessment pipeline can significantly improve the efficiency and consistency of clinical diagnosis and surgical planning. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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