Abdominal Aortic Aneurysm Segmentation from Contrast-Enhanced Computed Tomography Angiography Using Deep Convolutional Networks
| Autor: | Paweł Białas, Łukasz Znaniecki, Tomasz Dziubich, Joanna Halman, Jakub Brzeziński |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Aorta medicine.diagnostic_test business.industry Computer science Deep learning Image segmentation medicine.disease Convolutional neural network Abdominal aortic aneurysm Aortic aneurysm medicine.artery cardiovascular system medicine Segmentation Artificial intelligence Radiology business Computed tomography angiography |
| Zdroj: | ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium ISBN: 9783030558130 ADBIS/TPDL/EDA Workshops |
| DOI: | 10.1007/978-3-030-55814-7_13 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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