The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images

Autor: Yao-Wen Liang, Yu-Ting Fang, Ting-Chun Lin, Cheng-Ru Yang, Chih-Chang Chang, Hsuan-Kan Chang, Chin-Chu Ko, Tsung-Hsi Tu, Li-Yu Fay, Jau-Ching Wu, Wen-Cheng Huang, Hsiang-Wei Hu, You-Yin Chen, Chao-Hung Kuo
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Neurospine, Vol 21, Iss 2, Pp 665-675 (2024)
Druh dokumentu: article
ISSN: 2586-6583
2586-6591
DOI: 10.14245/ns.2448060.030
Popis: Objective This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion 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.
Databáze: Directory of Open Access Journals
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