Pre-operative Abdominal Aortic Aneurysm (AAA) 3D shape is critical for
customized stent-graft design in Fenestrated Endovascular Aortic Repair
(FEVAR). Traditional segmentation approaches implement expert-designed feature
extractors while recent deep neural networks extract features automatically
with multiple non-linear modules. Usually, a large training dataset is
essential for applying deep learning on AAA segmentation. In this paper, the
AAA was segmented using U-net with a small number (two) of training subjects.
Firstly, Computed Tomography Angiography (CTA) slices were augmented with gray
value variation and translation to avoid the overfitting caused by the small
number of training subjects. Then, U-net was trained to segment the AAA. Dice
Similarity Coefficients (DSCs) over 0.8 were achieved on the testing subjects.
The PLZ, DLZ and aortic branches are all reconstructed reasonably, which will
facilitate stent graft customization and help shape instantiation for
intra-operative surgery navigation in FEVAR.