Abstract：
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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：本研究旨在克服腰椎成像中的挑战，尤其是腰椎管狭窄,通过使用先进技术开发自动分割模型。传统的人工测量和病变检测方法受到主观性和低效率的限制。目的是创建准确且自动化的分割模型，以识别腰椎磁共振成像扫描中的解剖结构。
方法：利用539名腰椎管狭窄患者的数据集，本研究利用残差U-Net对腰椎矢状位和轴位磁共振图像进行语义分割。模型,训练来识别特定的组织类别，采用几何算法进行解剖结构量化。验证指标，比如联合交集(IOU)和骰子系数，验证残差U-Net的分割精度。引入了一种新颖的旋转矩阵方法来检测鼓起的圆盘，评估硬脑膜囊压迫，测量黄色韧带厚度。
结果：残余U-Net在分割腰椎结构方面实现了高精度，各种组织类别和视图的平均IOU值范围为0.82至0.93。自动量化系统提供椎间盘尺寸的测量，硬脑膜囊直径,黄色韧带厚度,和椎间盘水合作用。训练和测试数据集之间的一致性确保了自动测量的鲁棒性。
结论：具有残余U-Net和深度学习的自动腰椎分割在识别解剖结构方面具有很高的精度，促进腰椎管狭窄病例的有效量化。旋转矩阵的引入增强了病变检测，有希望提高诊断准确性，并支持腰椎管狭窄症患者的治疗决策。
方法：利用539名腰椎管狭窄患者的数据集，本研究利用残差U-Net对腰椎矢状位和轴位磁共振图像进行语义分割。模型,训练来识别特定的组织类别，采用几何算法进行解剖结构量化。验证指标，比如联合交集(IOU)和骰子系数，验证残差U-Net的分割精度。引入了一种新颖的旋转矩阵方法来检测鼓起的圆盘，评估硬脑膜囊压迫，测量黄色韧带厚度。
结果：残余U-Net在分割腰椎结构方面实现了高精度，各种组织类别和视图的平均IOU值范围为0.82至0.93。自动量化系统提供椎间盘尺寸的测量，硬脑膜囊直径,黄色韧带厚度,和椎间盘水合作用。训练和测试数据集之间的一致性确保了自动测量的鲁棒性。
结论：具有残余U-Net和深度学习的自动腰椎分割在识别解剖结构方面具有很高的精度，促进腰椎管狭窄病例的有效量化。旋转矩阵的引入增强了病变检测，有希望提高诊断准确性，并支持腰椎管狭窄症患者的治疗决策。
