Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects

Jian-Qing Zheng, Xiao-Yun Zhou, Qing-Biao Li, Celia V. Riga, Guang-Zhong Yang

Abstract: 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.