Department of Orthopaedics, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China.
Department of Orthopaedics, The Third Affiliated Hospital of Kunming Medical University (Tumor Hospital of Yunnan Province), Kunming 650000, China.
Contrast Media Mol Imaging. 2021 Aug 16;2021:1381274. doi: 10.1155/2021/1381274. eCollection 2021.
The aim of this study was to explore the adoption of the variable model algorithm in magnetic resonance imaging (MRI) image analysis and evaluate the effect of the algorithm-based MRI in the diagnosis of spinal metastatic tumor diseases. 100 patients with spinal metastatic tumors who were treated in hospital were recruited as the research objects. All patients were randomly divided into the experimental group (MRI image analysis based on variable model) and the control group (conventional MRI image diagnosis), and the MRI of the experimental group was segmented using the conventional algorithm with variable model and the improved algorithm with GVF force field. The accuracy index (Dice coefficient ) values were used to evaluate the vertebral segmentation effect of the improved variable model algorithm with the introduction of GVF force field, and the recognition rate, sensitivity, and specificity indexes were used to evaluate the effects of the two algorithms on the recognition of MRI image features of spinal metastatic tumors. The results showed that the mean value of the variable model improvement algorithm for the segmentation of five vertebrae of spinal metastatic tumors was significantly improved relative to the conventional variable model algorithm, and the difference was statistically significant ( < 0.05). At the number of 80 iterations, the recognition rate, sensitivity, and specificity of MRI image segmentation of the traditional variable model algorithm processing group were 89.32%, 74.88%, and 86.27%, respectively, while the recognition rate, sensitivity, and specificity of MRI image segmentation of the variable model improvement algorithm processing group were 97.89%, 96.75%, and 96.45%, respectively. The results of the latter were significantly better than those of the former, and the differences were statistically significant ( < 0.05); and the comparison of MRI images showed that the variable model improvement algorithm was more rapid and accurate in identifying the focal sites of patients with spinal metastases. The accuracy of MRI images based on the variable model algorithm increased from 69.5% to 92%, and the difference was statistically significant ( < 0.05). In short, MRI image analysis based on the variable model algorithm had great adoption potential in the clinical diagnosis of spinal metastatic tumors and was worthy of clinical promotion.
本研究旨在探讨可变模型算法在磁共振成像（MRI）图像分析中的应用，并评估基于该算法的 MRI 在诊断脊柱转移瘤疾病中的效果。选取我院收治的 100 例脊柱转移瘤患者作为研究对象，将所有患者随机分为实验组（基于可变模型的 MRI 图像分析）和对照组（常规 MRI 图像诊断），实验组采用常规算法加变分模型和引入 GVF 力场的改进算法对 MRI 进行分割。采用准确率（Dice 系数）值评估引入 GVF 力场的改进变分模型算法对椎体分割的效果，采用识别率、敏感度、特异度指标评估两种算法对脊柱转移瘤 MRI 图像特征识别的效果。结果显示，相对于常规变分模型算法，脊柱转移瘤 5 个椎体的变分模型改进算法的平均值明显提高，差异有统计学意义（ < 0.05）。在 80 次迭代的情况下，传统变分模型算法处理组的 MRI 图像分割的识别率、敏感度和特异度分别为 89.32%、74.88%和 86.27%，而变分模型改进算法处理组的 MRI 图像分割的识别率、敏感度和特异度分别为 97.89%、96.75%和 96.45%，后者明显优于前者，差异有统计学意义（ < 0.05）；且 MRI 图像对比显示，变分模型改进算法在识别脊柱转移瘤患者病灶部位时更加快速准确。基于变分模型算法的 MRI 图像准确率从 69.5%提高到 92%，差异有统计学意义（ < 0.05）。总之，基于可变模型算法的 MRI 图像分析在脊柱转移瘤的临床诊断中具有较大的采用潜力，值得临床推广。
