On the Automated Unruptured Intracranial Aneurysm Segmentation From TOF-MRA Using Deep Learning Techniques

Abstract

Aneurysms pose a life-threatening risk due to weakened vessel walls, causing bulging or ballooning in arterial blood vessels. The growth of an aneurysm increases the risk of rupture and consequent bleeding in the brain, leading to a hemorrhagic stroke. Therefore, accurate detection and segmentation of intracranial aneurysms are crucial for treatment planning in patients. Recently, the use of Time of Flight Magnetic Resonance Angiography (TOF-MRA) for automated segmentation of intracranial aneurysms has gained significant importance. This study comprehensively evaluates different automated segmentation methods for unruptured intracranial aneurysms, using the publicly available Aneurysm Detection and Segmentation (ADAM) challenge dataset. The performance and method scalability of these methods is analyzed across state-of-the-art algorithms, and the experimental analysis shows that 3D U-Net architecture outperforms in the segmentation tasks.

Keywords

• Aneurysm
• computer-aided detection
• time of flight-magnetic resonance angiography
• unruptured intracranial aneurysm
• dice similarity coefficient