College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China.
The Department of Medical Imaging Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
BMC Bioinformatics. 2022 Aug 5;23(1):322. doi: 10.1186/s12859-022-04872-y.
Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.
颅内动脉瘤破裂是蛛网膜下腔出血的首要原因，仅次于脑血栓形成和高血压性脑出血，死亡率非常高。MRI 技术在颅内动脉瘤的早期检测和诊断中发挥着不可替代的作用，支持评估动脉瘤的大小和结构。动脉瘤图像的增加，可能会给医生带来巨大的工作量，这很可能导致误诊。因此，我们提出了一种简单有效的综合残差注意网络（CRANet）来提高动脉瘤检测的准确性，使用残差网络来提取动脉瘤的特征。大量实验表明，所提出的 CRANet 模型可以有效地检测动脉瘤。此外，在测试集上，准确率和召回率分别达到了 97.81%和 94%，显著提高了动脉瘤的检出率。
