基于卷积神经网络的深度学习算法对颅内出血的类型识别及血肿分割一致性的研究

目的:开发一种可以检测不同类型颅内出血并自动计算血肿体积的基于卷积神经网络的深度学习算法,探讨其识别的准确性及血肿分割的一致性.方法:数据集1纳入9594例颅脑CT平扫图像,随机选取223例颅内出血阳性患者作为颅内出血类型识别的测试集,剩余CT图像作为其训练集,评估测试集中算法识别五种不同类型颅内出血的效能.数据集2选...

Evaluation of intracranial hemorrhage subtype recognition and hematoma segmentation consistency using convolution neural network

Objective:To develop a deep learning algorithm based on convolutional neural network to detect different types of intracranial hemorrhage and automatically calculate the volume of hematoma.And to explore its accuracy and consistency of hematoma segmentation.Methods:9594 cases of non-contrast head CT scan images were retrospectively included in Dataset 1.223 positive cases were randomly selected as the testing dataset for intracranial hemorrhage subtype recognition,and the rest CT images were used as the training dataset.The effectiveness of the test set algorithm in recognizing five different types of intracranial hemorrhage were evaluated.Another 819 manually labeled CT scans were enrolled in Dataset 2,from which 74 cases were randomly selected as the testing dataset.Manual segmentation was taken as the gold standard to verify the consistency of algorithm segmentation and manual segmentation in the test set.Results:Among 223 patients with positive intracranial hemorrhage,the area under the curve(AUC)for five types of intracranial hemorrhage in the subtype recognition testing dataset were all above or approximate to 0.85,and the specificity were greater than 0.95.Among the 74 cases of hematoma segmentation test data,there was a high consistency between the volume of hematoma automatically measured by the algorithm and the volume of hematoma manually measured by the algorithm.The intraclass correlation coefficients of intraparenchymal hemorrhage,epidural hemorrhage,intraventricular hemorrhage and subdural hemorrhage were 1,0.990,0.996 and 0.878 respectively in the hematoma segmentation testing dataset.Conclusion:Deep learning algorithm based on convolution neural network has good performance in intracranial hemorrhage recognition and hematoma segmentation,which has a certain prospect of clinical application.