We propose a deep learning-based technique for detection and quantification
of abdominal aortic aneurysms (AAAs). The condition, which leads to more than
10,000 deaths per year in the United States, is asymptomatic, often detected
incidentally, and often missed by radiologists. Our model architecture is a
modified 3D U-Net combined with ellipse fitting that performs aorta
segmentation and AAA detection. The study uses 321 abdominal-pelvic CT
examinations performed by Massachusetts General Hospital Department of
Radiology for training and validation. The model is then further tested for
generalizability on a separate set of 57 examinations with differing patient
demographics and acquisition characteristics than the original dataset. DeepAAA
achieves high performance on both sets of data (sensitivity/specificity
0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans
and works with image volumes with varying numbers of images. We find that
DeepAAA exceeds literature-reported performance of radiologists on incidental
AAA detection. It is expected that the model can serve as an effective
background detector in routine CT examinations to prevent incidental AAAs from
being missed.

本研究提出了一种基于深度学习的技术，用于检测和量化腹主动脉瘤，该模型架构是一个改进的 3D U-Net 算法与椭圆拟合相结合，可用于主动脉分割和腹主动脉瘤检测，模型经过 321 个腹部 - 盆腔 CT 检查的训练和验证，在 57 个新的检查数据上也具备很高的性能。