Segmentation Algorithm for Intracranial Magnetic Resonance Images for Cerebral Stroke Identification and Nursing Evaluation

Abstract

This study aimed to investigate the therapeutic effect and nursing evaluation of patients with cerebral stroke (CS) through intracranial magnetic resonance imaging (MRI) images under the condition of segmentation algorithm. 199 CS patients were selected and divided randomly into a control group (group A) and an experimental group (group B) based on different treatment methods. Patients of group A were given routine antithrombotic therapy, and patients of group B were treated with intravenous thrombolytic therapy under evaluation of segmentation algorithm-based MRI images. Then, there were comparisons on clinical therapeutic effect, neurological damage score, and daily living ability index score of all patients. After treatment, the total effective rate (92.12%) and daily life index (41.45 ± 11.24) of patients in group B were higher than those of group A ( P < 0.05). However, neurological damage scores (3.36 ± 1.13 points) of patients in group B after treatment were lower than those of group A (5.85 ± 2.31 points) ( P < 0.05). The routine clinical nursing satisfaction rate (79.8%) was lower than the overall satisfaction rate (97%) of the combination of clinical and imaging nursing ( P < 0.05). Therefore, there were greatly clinical therapeutic effects of intravenous thrombolysis evaluated by intracranial MRI images under segmentation algorithm for CS patients, and routine nursing could improve patients’ satisfaction, which were worthy of clinical promotion.