Abstract
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.
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Su, Z. et al. (2021). 3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_6
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