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Computerized Tomography Angiography (CTA) based follow-up of Abdominal AorticAneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essentialto evaluate the progress of the patient and detect complications. In thiscontext, accurate quantification of post-operative thrombus volume is required.However, a proper evaluation is hindered by the lack of automatic, robust andreproducible thrombus segmentation algorithms. We propose a new fully automaticapproach based on Deep Convolutional Neural Networks (DCNN) for robust andreproducible thrombus region of interest detection and subsequent fine thrombussegmentation. The DetecNet detection network is adapted to perform region ofinterest extraction from a complete CTA and a new segmentation networkarchitecture, based on Fully Convolutional Networks and a Holistically-NestedEdge Detection Network, is presented. These networks are trained, validated andtested in 13 post-operative CTA volumes of different patients using a 4-foldcross-validation approach to provide more robustness to the results. Ourpipeline achieves a Dice score of more than 82% for post-operative thrombussegmentation and provides a mean relative volume difference between groundtruth and automatic segmentation that lays within the experienced humanobserver variance without the need of human intervention in most common cases.

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks