Multiple angle key points detection guided screening of unruptured intracranial aneurysms

Early screening of unruptured intracranial aneurysms is critical for disease control so as to attract a lot of attention. However, the extremly small size of lesions and large variance of appearances pose difficulties in algorithm modeling. To tackle this challenge, the paper proposes a multiple angle key points detection guided screening method for localization and segmentation of intracranial aneurysms. The proposed method consists of two modules, multiple angle key points detection and multi-task learning based segmentation. The key points detection is performed on multiple projection directions thus to localize aneurysms candidates. Once obtaining region of interest patches, segmentation models constrained by parallel multi downstream task headers perform the delineation accordingly. Validation has been performed on a dataset containing computational tomograhpic angiography scans of patients with intracranial aneurysms. Results have shown that the proposed method could significantly improve segmentation and detection performance from target-wise and voxel-wise point of views, which have also demonstrated its effectiveness and application prospects.