Vessel-aware aneurysm detection using multi-scale deformable 3D attention.

Abstract

Manual detection of intracranial aneurysms (IAs) in computed tomography (CT) scans is a complex, time-consuming task even for expert clinicians, and automating the process is no less challenging. Critical difficulties associated with detecting aneurysms include their small (yet varied) size compared to scans and a high potential for false positive (FP) predictions. To address these issues, we propose a 3D, multi-scale neural architecture that detects aneurysms via a deformable attention mechanism that operates on vessel distance maps derived from vessel segmentations and 3D features extracted from the layers of a convolutional network. Likewise, we reformulate aneurysm segmentation as bounding cuboid prediction using binary cross entropy and three localization losses (location, size, IoU). Given three validation sets comprised of 152/138/38 CT scans and containing 126/101/58 aneurysms, we achieved a Sensitivity of 91.3%/97.0%/74.1% @ FP rates 0.53/0.56/0.87, with Sensitivity around 80% on small aneurysms. Manual inspection of outputs by experts showed our model only tends to miss aneurysms located in unusual locations. Code and model weights are available online.