Convolutional Neural Network based Segmentation of Abdominal Aortic Aneurysms.

Abstract

Abdominal aortic aneurysms (AAAs) are balloonlike dilations in the descending aorta associated with high mortality rates. Between 2009 and 2019, reported ruptured AAAs resulted in ~28,000 deaths while reported unruptured AAAs led to ~15,000 deaths. Automating identification of the presence, 3D geometric structure, and precise location of AAAs can inform clinical risk of AAA rupture and timely interventions. We investigate the feasibility of automatic segmentation of AAAs, inclusive of the aorta, aneurysm sac, intra-luminal thrombus, and surrounding calcifications, using 30 patient-specific computed tomography angiograms (CTAs). Binary masks of the AAA and their corresponding CTA images were used to train and test a 3D U-Net - a convolutional neural network (CNN) - model to automate AAA detection. We also studied model-specific convergence and overall segmentation accuracy via a loss-function developed based on the Dice Similarity Coefficient (DSC) for overlap between the predicted and actual segmentation masks. Further, we determined optimum probability thresholds (OPTs) for voxel-level probability outputs of a given model to optimize the DSC in our training set, and utilized 3D volume rendering with the visualization tool kit (VTK) to validate the same and inform the parameter optimization exercise. We examined model-specific consistency with regard to improving accuracy by training the CNN with incrementally increasing training samples and examining trends in DSC and corresponding OPTs that determine AAA segmentations. Our final trained models consistently produced automatic segmentations that were visually accurate with train and test set losses in inference converging as our training sample size increased. Transfer learning led to improvements in DSC loss in inference, with the median OPT of both the training segmentations and testing segmentations approaching 0.5, as more training samples were utilized.