Adoption of Magnetic Resonance Image Features under Segmentation Algorithm in Effect Evaluation of Ginkgo Diterpenoid Lactone Glucamine Injection in Treatment of Cerebral Infarction

• Published in: Contrast media and molecular imaging 2022, Vol.2022 (1), p.4558702-4558702
• Main Authors: Lei, Aidi, Zhang, Yanbo, Liang, Fulong, Zhang, Jianli, Cai, Jinle
• Format: Article
• Language: English
• Subjects: Algorithms; Brain Ischemia; Cerebral Infarction - diagnostic imaging; Cerebral Infarction - drug therapy; Diterpenes; Ginkgo biloba; Humans; Image Processing, Computer-Assisted; Lactones; Magnetic Resonance Imaging - methods; Meglumine; Vascular Endothelial Growth Factor A
• ISSN: 1555-4309; 1555-4317
• DOI: 10.1155/2022/4558702

Magnetic resonance imaging (MRI) image segmentation based on a segmentation algorithm was performed to assess neurological function in patients with acute cerebral infarction, to investigate the efficacy evaluation of Ginkgo diterpene lactones meglumine injection (GDLI) in the treatment of cerebral infarction and the efficiency of MRI image segmentation algorithm. First, the results of the fast semisupervised segmentation algorithm (algorithm group) and traditional processing (control group) were compared and analyzed. The recall rate, accuracy, recognition accuracy, and segmentation time of the two groups were compared. The control group was given conventional treatment, while the algorithm group was given GDLI based on conventional treatment. Finally, the difference in serum vascular endothelial growth factor (VEGF), hypoxia-inducible factor-la (HIF-la), angiotensin (Ang)-1, Ang-2, and interleukin (IL)-6 protein concentration was analyzed after treatment. The algorithm evaluation results showed that the accuracy and recall rate of MRI images recognized by the algorithm group fluctuate at 90%. In the control group, the accuracy and recall rate of MRI image results fluctuated at 80%, and the data were statistically different (p