Neural Network Segmentation Algorithm-Based Magnetic Resonance Imaging to Explore the Relationship between Cerebrospinal Fluid Flow with Communicating Hydrocephalus after Decompressive Craniectomy for Craniocerebral Injury

Abstract

This study aimed to explore the application value of magnetic resonance imaging optimized by neural network segmentation algorithm in analyzing the relationship between cerebrospinal fluid changes after decompressive craniectomy and the occurrence of communicating hydrocephalus. 100 patients with craniocerebral injury undergoing decompressive craniectomy in hospital were selected as research subjects. The collected MRI images were processed using the OTSU algorithm, the cerebrospinal fluid flow rate was calculated based on the observation results, and the MRI based on the neural network segmentation algorithm was used to analyze the relationship between the occurrence of communicating hydrocephalus with the cerebrospinal fluid flow after decompressive craniectomy for craniocerebral injury. Additionally, the dynamics of the flow of cerebrospinal fluid in the midbrain aqueduct was analyzed. After decompressive craniectomy for craniocerebral injury, of the 24 cases of cerebrospinal fluid accumulation, 23 cases had hydrocephalus; of the 55 cases of cerebrospinal fluid flow disorder, hydrocephalus occurred in 47 cases; and of the 21 cases of normal cerebrospinal fluid, no patients had hydrocephalus. For patients with communicating hydrocephalus, the cerebrospinal fluid flow at the aqueduct was obviously accelerated and the flow was increased. From this, the differential diagnosis of cerebrospinal fluid and communicating hydrocephalus can be further confirmed. The results showed that the third ventricle of the study group was significantly reduced, and the flow of cerebrospinal fluid was similar to that of normal people. It suggested that decompressive craniectomy can relieve communicating hydrocephalus. In patients with communicating hydrocephalus, the cerebrospinal fluid flow at the aqueduct was significantly accelerated, the flow amount was increased, and the blocked flow of cerebrospinal fluid can also lead to hydrocephalus, which further clarified the relationship between the occurrence of communicating hydrocephalus with the flow of cerebrospinal fluid. In short, the neural network segmentation algorithm-based magnetic resonance imaging demonstrated a good value in the analysis of craniocerebral injury, from which the doctor observed that the cerebrospinal fluid flow at the aqueduct was significantly accelerated. Its detection of brain complications after decompressive craniectomy was also effective.