Abstract
Computerized Tomography Angiography (CTA) based assessment of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential during follow-up to evaluate the progress of the patient along time, comparing it to the pre-operative situation, and to detect complications. In this context, accurate assessment of the aneurysm or thrombus volume pre- and post-operatively is required. However, a quantifiable and trustworthy evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose an automatic pipeline for thrombus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.
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López-Linares, K. et al. (2017). DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_4
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DOI: https://doi.org/10.1007/978-3-319-67534-3_4
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