Yu Liu1, Qiong Fang2, Anhong Jiang3, Qingling Meng4, Gang Pang5, Xuefei Deng6. 1. Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China. Electronic address: yo_well@163.com. 2. Department of Basic Medicine, Anhui Medical College, Hefei 230601, China. Electronic address: fangqamc@163.com. 3. Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Electronic address: janhongf@sina.com. 4. Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China. Electronic address: ql-meng@163.com. 5. Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China. Electronic address: gangpang1975@aliyun.com. 6. Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China. Electronic address: dengxf@ahmu.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: Early hemorrhage enlargement in hypertensive cerebral hemorrhage indicates a poor prognosis. This study aims to predict the early enlargement of cerebral hemorrhage through the intelligent texture analysis of cerebral hemorrhage after segmentation. METHODS: A total of 54 patients with hypertensive intracerebral hemorrhage were selected and divided into enlarged hematoma (enlarged group) and non-enlarged hematoma (negative group). The U-Net Neural network model and contour recognition were used to extract the brain parenchymal region, and Mazda texture analysis software was used to extract regional features. The texture features were reduced by Fisher coefficient (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) to select the best feature parameters. B11 module was used to analyze the selected features. The misclassified rate of feature parameters screened by different dimensionality reduction methods was calculated. RESULTS: The neural network based on U-Net can accurately identify the lesion of cerebral hemorrhage. Among the 54 patients, 18 were in the enlarged group and 36 in the negative group. The parameters of gray level co-occurrence matrix and gray level run length matrix can be used to predict the enlargement of intracerebral hemorrhage. Among the features screened by Fisher, POE + ACC and MI, the texture features of MI showed the lowest misclassified rate, which was 0. CONCLUSION: The texture analysis based on U-Net neural network is helpful to predict the early expansion of hypertensive cerebral hemorrhage, and the parameters of gray level co-occurrence matrix and gray level run length matrix under MI dimensionality reduction have the most excellent predictive value.
BACKGROUND AND OBJECTIVE: Early hemorrhage enlargement in hypertensive cerebral hemorrhage indicates a poor prognosis. This study aims to predict the early enlargement of cerebral hemorrhage through the intelligent texture analysis of cerebral hemorrhage after segmentation. METHODS: A total of 54 patients with hypertensive intracerebral hemorrhage were selected and divided into enlarged hematoma (enlarged group) and non-enlarged hematoma (negative group). The U-Net Neural network model and contour recognition were used to extract the brain parenchymal region, and Mazda texture analysis software was used to extract regional features. The texture features were reduced by Fisher coefficient (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) to select the best feature parameters. B11 module was used to analyze the selected features. The misclassified rate of feature parameters screened by different dimensionality reduction methods was calculated. RESULTS: The neural network based on U-Net can accurately identify the lesion of cerebral hemorrhage. Among the 54 patients, 18 were in the enlarged group and 36 in the negative group. The parameters of gray level co-occurrence matrix and gray level run length matrix can be used to predict the enlargement of intracerebral hemorrhage. Among the features screened by Fisher, POE + ACC and MI, the texture features of MI showed the lowest misclassified rate, which was 0. CONCLUSION: The texture analysis based on U-Net neural network is helpful to predict the early expansion of hypertensive cerebral hemorrhage, and the parameters of gray level co-occurrence matrix and gray level run length matrix under MI dimensionality reduction have the most excellent predictive value.