PDF(Pubmed)
Abstract：
BACKGROUND: Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals.
METHODS: This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy.
RESULTS: Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations.
CONCLUSIONS: Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.
METHODS: This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy.
RESULTS: Analysis reveals a progressive increase in the size of vertebrae and intervertebral discs from the cervical to lumbar regions. Vertebrae, bearing weight and safeguarding the spinal cord and nerves, are interconnected by intervertebral discs, resilient structures that counteract spinal pressure. Experimental findings demonstrate a lack of pronounced anteroposterior bending during flexion and extension, maintaining displacement and rotation angles consistently approximating zero. This consistency maintains uniform anterior and posterior vertebrae heights, coupled with parallel intervertebral disc heights, aligning with theoretical expectations.
CONCLUSIONS: Accuracy assessment employs 2 methods: IoU and Dice, and the average accuracy of IoU is 88% and that of Dice is 96.4%. The proposed deep learning-based system showcases promising results in spinal canal segmentation, laying a foundation for precise stenosis diagnosis in computed tomography images. This contributes significantly to advancements in spinal pathology understanding and treatment.
摘要：
背景：腰椎间盘突出症被认为是一种与年龄相关的退行性疾病。然而，新出现的报告突出了一个明显的转变，说明了这些疾病在年轻人中的患病率。
方法：本研究介绍了一种为椎管分割和疾病诊断量身定制的新型深度学习方法。强调深入研究灰度等基本图像属性的图像处理技术，纹理,和统计结构来提高分割精度。
结果：分析显示，从颈椎到腰椎，椎骨和椎间盘的大小逐渐增加。椎骨,负重并保护脊髓和神经，通过椎间盘相互连接，抵消脊柱压力的弹性结构。实验结果表明，在屈曲和伸展过程中缺乏明显的前后弯曲，保持位移和旋转角度始终接近零。这种一致性保持一致的前后椎骨高度，加上平行的椎间盘高度，与理论预期保持一致。
结论：准确性评估采用两种方法：IoU和Dice，IoU的平均准确率为88%，Dice的平均准确率为96.4%。提出的基于深度学习的系统在椎管分割方面展示了有希望的结果，为CT图像中狭窄的精确诊断奠定了基础。这大大有助于脊柱病理学理解和治疗的进步。
方法：本研究介绍了一种为椎管分割和疾病诊断量身定制的新型深度学习方法。强调深入研究灰度等基本图像属性的图像处理技术，纹理,和统计结构来提高分割精度。
结果：分析显示，从颈椎到腰椎，椎骨和椎间盘的大小逐渐增加。椎骨,负重并保护脊髓和神经，通过椎间盘相互连接，抵消脊柱压力的弹性结构。实验结果表明，在屈曲和伸展过程中缺乏明显的前后弯曲，保持位移和旋转角度始终接近零。这种一致性保持一致的前后椎骨高度，加上平行的椎间盘高度，与理论预期保持一致。
结论：准确性评估采用两种方法：IoU和Dice，IoU的平均准确率为88%，Dice的平均准确率为96.4%。提出的基于深度学习的系统在椎管分割方面展示了有希望的结果，为CT图像中狭窄的精确诊断奠定了基础。这大大有助于脊柱病理学理解和治疗的进步。
