MVRNet: A Multi-View Refinement Network for Accurate Recognition of Challenging Intracranial Aneurysms in Enhanced 3D CTA Images

摘要

Intracranial aneurysms are life-threatening and require accurate, timely detection. Traditional manual diagnosis by radiologists can be subjective, leading to misdiagnoses, while existing deep learning approaches struggle with small aneurysms or cases complicated by surrounding tissues. In this paper, we present the Multi-View Refinement Network (MVRNet), a framework that delivers clinically meaningful performance gains on the most challenging intracranial aneurysm cases. It directly addresses real-world diagnostic difficulties, particularly for small, obscured, or ambiguously bounded lesions that conventional methods often miss. In particular, we propose a feature enhancement technique to obtain boundary-clear CTA, an informative supplement to the bone-free CTA, to further reduce the impact of noise and enhance the robustness of IA recognition. Moreover, we develop a multi-view encoder with 2D slicing in different directions to mitigate tissue occlusion effects, coupled with a progressively refined decoder that iteratively corrects uncertain predictions, ensuring precise localization and segmentation. Experimental results indicate that MVRNet improves the F1-score by 24% and the IoU by 16% in comparison with the state-of-the-art methods. Remarkably, previous methods struggled with challenging cases, while our method still achieves satisfactory results, demonstrating a >2× improvement over prior arts on challenging test sets. Our code has been released in https://github.com/TheResearchWorks/MVRNet.