Mathematical and Numerical Methods for Accurate Aorta Segmentation from Non-Enhanced ct Data Yielding Reliable Identification and Evaluation of Large Vessel Vasculitis

Abstract Segmentation of the aorta is crucial for various medical analyses, such as the diagnosis and treatment of cardiovascular diseases. This work presents mathematical models and methods yielding a semi-automatic segmentation of the aorta from non-enhanced CT data. Our framework consists of three steps. First, using the minimal path approach, we extract a path within the aorta from two user-supplied points. Then, using 3D Lagrangian curve evolution, we move the initial path to the approximate centerline of the aorta. The centered path is used in the last step to construct the initial condition for the generalized subjective surface method (GSUBSURF). Applying the GSUBSURF method with this initial condition yields an accurate segmentation of the aorta. The segmentation results and the manual segmentations overlap, with a worst-case mean Hausdorff distance of 2.175 ± 0.605 mm for a voxel spacing of 0.977 mm. Using the aorta centerline and segmentation, we define precise regions of interest along the aorta to assess large-vessel vasculitis from patient FDG-PET/CT image data. The application shows promising results, as we demonstrated widespread inflammation throughout the aorta in a patient prior to treatment. After treatment, we observed a significant reduction in inflammation and accurately identified the aortic regions where it persisted. These findings also align with those of experienced medical doctors who have worked on the same cases.