多任务学习实现颅内动脉瘤精准分类与分割，提升诊疗决策支持能力。
Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
- 采用双阶段框架：2D三轴ROI快速定位 + 3D多任务nnU-Net建模
- 在RSNA 2025挑战赛中获第二名，支持13个解剖位置和4种影像模态
- 设计双解码器缓解病灶与血管体积差异大问题，适合临床辅助诊断
颅内动脉瘤常无症状，破裂后致死率高。风险评估与治疗规划依赖于动脉瘤形态与解剖位置，但现有自动化方法仅限于二分类检测，缺乏细粒度解剖分类或多类别分割能力。本文提出一种多任务框架，同时实现多标签分类、多类别动脉瘤分割及多类别血管分割，覆盖13个解剖部位和四种成像模态（CTA、MRA、T2、T1-post）。采用两阶段方法：结合快速2D三轴感兴趣区域（ROI）提取与3D多任务nnU-Net主干网络。双解码器设计缓解动脉瘤与血管类别间极端体积不平衡问题；跨注意力池化与模态特异性辅助头增强异质输入下的特征学习能力。所提两重集成方法在RSNA 2025颅内动脉瘤检测挑战赛中取得第二名。代码、模型权重、3D Slicer插件及修正分割标签均已公开。
原文摘要 · Abstract (English)
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and anatomical location, yet existing automated methods remain limited to binary detection without fine-grained anatomical classification or multi-class segmentation. We present a multi-task framework that simultaneously performs multi-label classification, multi-class aneurysm segmentation, and multi-class vessel segmentation across 13 anatomical locations and four imaging modalities (CTA, MRA, T2, T1-post). Our two-stage approach combines a fast 2D tri-axial Region of Interest (ROI) extraction method with a 3D multi-task nnU-Net backbone. A dual-decoder design mitigates the extreme volume imbalance between aneurysm and vessel classes, while cross-attention pooling and modality-specific auxiliary heads improve feature learning across heterogeneous inputs. Our two-fold ensemble achieved 2nd place in the RSNA 2025 Intracranial Aneurysm Detection challenge. Code, model weights, a 3D Slicer plugin, and the corrected segmentation labels are publicly available.
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