Enhancing Aorta Segmentation in Contrast CT Images: A Novel Deep Architectural Approach

Manual segmentation of patient CT images is both time-consuming and labor-intensive. Additionally, classic image processing techniques are insufficient in CT images due to the close pixel values of tissues. Automatic segmentation of the aorta in human anatomy can reduce healthcare workers' workload in preoperative planning. This study compares the performance of the AKG-Unet segmentation model with other models (U-Net, Inception UNetv2, LinkNet, SegNet, and Res-Unet) on thoracic aorta, abdominal aorta, and iliac arteries segmentation in contrast CT images. Initially, pixel intensities in the Kits and Rider datasets were recalibrated. Then, 2D axial images underwent resizing and grayscale normalization. Segmentation models have been trained and tested with 5-fold cross-validation. 2D prediction masks were stacked to generate a 3D output, and spatial information was transferred to the predicted mask. In the 3B aortic segmentation, small objects adjacent to it were removed using image processing techniques. In our study, the AKG-UNET model achieved the highest segmentation results on the AVT dataset with a Dice score of 91.2%, Intersection-Over-Union (IoU) score of 85.6%, sensitivity of 90.9%, and specificity of 99%. A method has been proposed that helps physicians analyze the aortic structure, and segments the aortic structure so that they can intervene in the correct location and make a preoperative evaluation.