The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images.
Yao-Wen LiangYu-Ting FangTing-Chun LinCheng-Ru YangChih-Chang ChangHsuan-Kan ChangChin-Chu KoTsung-Hsi TuLi-Yu FayJau-Ching WuWen-Cheng HuangHsiang-Wei HuYou-Yin ChenChao-Hung KuoPublished in: Neurospine (2024)
Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.
Keyphrases
- deep learning
- convolutional neural network
- magnetic resonance
- artificial intelligence
- end stage renal disease
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- high resolution
- peritoneal dialysis
- prognostic factors
- magnetic resonance imaging
- computed tomography
- high throughput
- patient reported outcomes
- combination therapy
- quantum dots