756 A 3D Deep-learning Based Algorithm for Cerebral Vessel and Aneurysm Segmentation From CTA Images

INTRODUCTION: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. METHODS: Using 50 paired CTA-DSA images, we trained (n = 27), validated (n = 3) and tested (n = 20) a deep-learning model (3D-DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based co-registration algorithm was used for registration and up-sampling of CTAs to paired DSAs. Segmented vessels from DSAs were used as the ground-truth. Accuracy of the model was evaluated using conventional metrics (dice similarity coefficient-DSC) and vessel-specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 experts detected and segmented IAs. For each rater, we recorded the rate of IA detection. For detected IAs, raters segmented IAs and calculated morphology parameters to quantify the differences in IA segmentation by raters to DeepMedic. The rater agreements were assessed using Krippendorf’s alpha. RESULTS: In testing, the DeepMedic model yielded a DSC and CAL of 0.971 ± 0.007 and 0.868 ± 0.008, respectively. The model delineated all IAs with errors <10% for all IA morphometrics. Conversely, average IA detection accuracy from raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA cavernous region and were significantly smaller). Error rates for IA morphometrics in rater-segmentations were significantly higher than in DeepMedic-segmentations, particularly for neck (p = 0.003), and surface-area (p = 0.003). For IA morphology, agreement between the raters was acceptable for metrics except UI (α=0.36) and NSI (α=0.69). The agreement between DeepMedic and ground truth was consistently higher. CONCLUSIONS: Our CTA segmentation algorithm provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.