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.

中文翻译：

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基于U-Net神经网络的纹理分析用于颅内出血鉴定可预测早期肿大

背景和目标

高血压脑出血的早期出血扩大表明预后不良。本研究旨在通过对分割后脑出血的智能纹理分析来预测脑出血的早期扩大。

方法

选择54例高血压性脑出血患者，将其分为扩大血肿（扩大组）和非扩大血肿（阴性组）。使用U-Net神经网络模型和轮廓识别来提取脑实质区域，并使用Mazda纹理分析软件来提取区域特征。通过Fisher系数（Fisher），分类错误概率，平均相关系数（POE + ACC）和互信息（MI）来减少纹理特征，以选择最佳特征参数。B11模块用于分析所选功能。计算了通过不同降维方法筛选出的特征参数的误分类率。

结果

基于U-Net的神经网络可以准确识别脑出血的病灶。在54例患者中，扩大组18例，阴性组36例。灰度共生矩阵和灰度游程长度矩阵的参数可用于预测脑出血的扩大。在Fisher，POE + ACC和MI筛选的特征中，MI的纹理特征显示出最低的误分类率，为0。

结论

基于U-Net神经网络的纹理分析有助于预测高血压脑出血的早期扩展，在MI维数降低的情况下，灰度共生矩阵和灰度游程矩阵的参数具有最佳的预测价值。