A 3D CNN Prediction of Cerebral Aneurysm in the Bifurcation Region of Interest in Magnetic Resonance Angiography

Quantitative vascular analysis involves the measurements of arterial tortuosity and branch angle in a region of interest in cerebral arteries to assess vascular risks associated with cerebral aneurysm. The measurements themselves are not a simple process since they are made on the three-dimensional (3D) structures of the arteries. The aim of this study was to develop a deep convolutional neural network (CNN) model to predict a probability score of aneurysm without direct measurements of the artery’s geometry. A total of 204 subjects’ image data were considered. In all, 585 gray-scale three-dimensional (3D) patches with the bifurcations near the center of the patches were extracted and labeled as either an aneurysm or a non-aneurysm class. Three-dimensional CNN architectures were developed and validated for the binary classification of the 3D patches. Accuracy, precision, recall, F1-score, receiver operating characteristics area under the curve (ROC-AUC), and precision recall AUC (PR-AUC) were calculated for test data. Deep learning predictions were compared with vessel geometry measurements. Deep learning probability scores were dichotomized into high-score and low-score groups. For both groups, bifurcation angles and sum-of-angles-metric (SOAM) were calculated and compared. ResNetV2_18 with translation as data augmentation achieved the highest mean ROC-AUC (0.735) and PR-AUC (0.472). The independent t-test indicated that for the bifurcation angle sum feature there was a statistically significant difference (t = −2.280, p-value < 0.05) between the low-score and the high-score groups. In conclusion, we have demonstrated a deep learning-based approach to the prediction of aneurysmal risks in the bifurcation regions of interest. Deep learning predictions were associated with vessel geometry measurements. This suggests that deep learning on 3D patches centered around the bifurcations has the potential to screen bifurcations with a high aneurysm risk.