One of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying U-Net, ResNet, and VBNet frameworks. Our results show that we are able to outperform state-of-the-art methods by 3% on the Dice metric without any additional post-processing steps.
Authors
- dr inż. Tomasz Dziubich link open in new tab ,
- Paweł Białas,
- dr n. med Łukasz Znaniecki,
- Joanna Halman,
- Jakub Brzeziński
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-030-55814-7_13
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2020
