Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks

Tomasz Dziubich; Paweł Białas; Łukasz Znaniecki; Joanna Halman; Jakub Brzeziński

doi:10.1007/978-3-030-55814-7_13

Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks

Abstract

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.

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Authors (5)

Tomasz Dziubich dr inż.
Paweł Białas
Łukasz Znaniecki dr n. med
Joanna Halman
Jakub Brzeziński

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Category:: Conference activity
Type:: publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:: English
Publication year:: 2020
Bibliographic description:: Dziubich T., Białas P., Znaniecki Ł., Halman J., Brzeziński J.: Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks// / : , 2020,
DOI:: Digital Object Identifier (open in new tab) 10.1007/978-3-030-55814-7_13
Verified by:: Gdańsk University of Technology