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

Background: Endovascular aortic aneurysm repair (EVAR) is currently established as the first-line treatment for anatomically suitable abdominal aortic aneurysm (AAA).

Objective: To establish a deep convolutional neural networks (DCNN) model for fully automatic segmentation intraluminal thrombosis (ILT) of abdominal aortic aneurysm (AAA) in pre-operative computed tomography angiography (CTA) images.

Methods: We retrospectively reviewed 340 patients of AAA with ILT at our single center. The software ITKSNAP was used to draw AAA and ILT region of interests (ROIs), respectively. Image preprocessing and DCNN model build using MATLAB. Randomly divided, 80% of patients was classified as training set, 20% of patients was classified as test set. Accuracy, intersection over union (IOU), Boundary F1 (BF) Score were used to evaluate the predictive effect of the model.

Results: By training in 34760-35652 CTA images (n= 204) and validation in 6968-7860 CTA images (n=68), the DCNN model achieved encouraging predictive performance in test set (n= 68, 6898 slices): Global accuracy 0.9988 ± 5.7735E-05, mean accuracy 0.9546 ± 0.0054, ILT IOU 0.8650 ± 0.0033, aortic lumen IOU 0.8595 ± 0.0085, ILT weighted IOU 0.9976 ± 0.0001, mean IOU 0.9078 ± 0.0029, mean BF Score 0.9829 ± 0.0011. Our DCNN model achieved a mean IOU of more than 90.78% for segmentation of ILT and aortic lumen. It provides a mean relative volume difference between automatic segmentation and ground truth (P> 0.05).

Conclusion: An end-to-end DCNN model could be used as an efficient and adjunctive tool for fully automatic segmentation of abdominal aortic thrombus in pre-operative CTA image.