Exploration of CT Images Based on the BN-U-net-W Network Segmentation Algorithm in Glioma Surgery

This study aimed to explore the application value of computed tomography (CT) imaging features based on the deep learning batch normalization (batch normalization, BN) U-net-W network image segmentation algorithm in evaluating and diagnosing glioma surgery. 72 patients with glioma who were admitted to hospital were selected as the research subjects. They were divided into a low-grade group (grades I-II, N = 27 cases) and high-grade group (grades III-IV, N = 45 cases) according to postoperative pathological examination results. The CT perfusion imaging (CTPI) images of patients were processed by using the deep learning-based BN-U-net-W network image segmentation algorithm. The application value of the algorithm was comprehensively evaluated by comparing the average Dice coefficient, average recall rate, and average precision of the BN-U-net-W network image segmentation algorithm with the U-net and BN-U-net network algorithms. The results showed that the Dice coefficient, recall, and precision of the BN-U-net-W network were 86.31%, 88.43%, and 87.63% respectively, which were higher than those of the U-net and BN-U-net networks, and the differences were statistically significant (P < 0.05). Cerebral blood flow (CBF), cerebral blood volume (CBV), and capillary permeability (PMB) in the glioma area were 56.85 mL/(min·100 g), 18.03 mL/(min·100 g), and 8.57 mL/100 g, respectively, which were significantly higher than those of normal brain tissue, showing statistically significant differences (P < 0.05). The mean transit time (MTT) difference between the two was not statistically significant (P > 0.05). The receiver operating characteristic (ROC) curves of CBF, CBV, and PMB in CTPI parameters of glioma had area under the curve (AUC) of 0.685, 0.724, and 0.921, respectively. PMB parameters were significantly higher than those of CBF and CVB, and the differences were statistically obvious (P < 0.05). It showed that the BN-U-net-W network model had a better image segmentation effect, and CBF, CBV, and PMB showed better sensitivity in diagnosing glioma tissue and normal brain tissue and high-grade and low-grade gliomas, among which PBM showed the highest predictability.