Cerebrovascular Landmark Detection Under Anatomical Variations

Anatomical landmark detection has important applications in cerebrovascular analysis and clinical treatments, which is challenging due to the complex structure, various natural variations and pathological changes. In this paper, we propose a multi-task deep learning network for accurate detection of 19 landmarks in cerebral Magnetic Resonance Angiography (MRA) images, which is robust to anatomical variations. Besides landmark detection, the network is trained to perform landmark attribute classification, semantic artery segmentation and arterial segment attribute classification simultaneously. The attributes of landmark and arterial segment are defined as local bifurcation appearance and absence variation, respectively, which enhances the contextual information and incorporates the structural prior knowledge explicitly. Experiments on both public and private datasets demonstrate the superior performance of the proposed method.