Enriching computed tomography images by projection for robust automated cerebral aneurysm detection and segmentation

Developing an efficient system for automated detection and segmentation of intracranial aneurysms (IAs) became an active research topic recently. However, existing methods are poor in detecting small IA with high false positives. In this paper, we present a feature enrichment (FE) based deep learning method for robust IA detection and segmentation. The FE technique is featured by reconstructing a 3D model from all computed tomography angiography (CTA) images and then projecting 3D information into the so-called projection images of different slicing levels along various directions. The appearances of aneurysms in the projection images are enhanced in morphology and 3D neighborhood features, and thus are easy to be detected and segmented by the widely-used faster RCNN and V-Net. To evaluate our method, we collect CTA images from 145 patients (including 148 IAs) for training and testing. The proposed method achieves 96.0 % sensitivity for all aneurysms, and 80.0% sensitivity for aneurysms smaller than 4mm, better than the state-of-the-art methods. Also, the segmentation performance is improved on the detected IAs.