Early detection and accurate segmentation of cerebral aneurysm is important for clinical diagnosis and prevention of rupture, which would be life threatening. 3D images can provide abundant information for characterizing the aneurysm. But the traditional manual segmentation of aneurysms takes lots of time and effort. Therefore, accurate and rapid automatic algorithm for 3D segmentation of aneurysm is needed. U-Net is a widely used deep learning network in medical image segmentation, but its performance is limited by the amount of data. In this challenge of aneurysm segmentation, we proposed to add attention gate and Models Genesis pretraining mechanisms to the classical U-Net model to improve the results. The dice of 3D U-net, 3D Attention U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in segmenting aneurysms. This work achieved rank one in CADA 2020-Aneurysm Segmentation Challenge.
Early diagnosis and treatment of cerebral aneurysms are important for reducing the risk of aneurysm rupture. Fast and accurate detection of aneurysms on blood vessels is a key step in diagnosis of aneurysm. To date, a large number of deep learning algorithms, especially the UNet network, have been developed for detection of aneurysms. However, when the amount of data for training is small, it is difficult to obtain a reliable deep learning network to effectively identify aneurysms. In order to address this issue and improve the accuracy of aneurysm detection, here we proposed to combine the deep learning approach with specially designed preprocessing and postprocessing algorithm. We first determined the rough locations of the aneurysms based on the features on the vascular skeleton before aneurysms segmentation with deep learning network, i.e. 3D Attention UNet in this work, thus reducing the missed detection rate of the UNet network.We could obtain the shape and texture related to the aneurysm. Then we used the random forest algorithm to implement the feature classification model to find out the false aneurysms incorrectly detected by the U-Net network. The experimental results show that our method can accurately identify aneurysms in the case of small data sets.
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