bibtext: @InProceedings{ Ceb_Vesselaware_MICCAI2024,
                   author = { Ceballos-Arroyo, Alberto M. and Nguyen, Hieu T. and Zhu, Fangrui and Yadav, Shrikanth M. and Kim, Jisoo and Qin, Lei and Young, Geoffrey and Jiang, Huaizu },
                   title = { { Vessel-aware aneurysm detection using multi-scale deformable 3D attention } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Manual detection of intracranial aneurysms 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.
title: Vessel-aware aneurysm detection using multi-scale deformable 3D attention
authors: Ceballos-Arroyo, Alberto M. Nguyen, Hieu T. Zhu, Fangrui Yadav, Shrikanth M. Kim, Jisoo Qin, Lei Young, Geoffrey Jiang, Huaizu