The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the task becomes challenging. These shifts include differences in image appearance, intensity distribution, resolution, and aneurysm size, all of which complicate the segmentation process. To tackle these issues, we propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique coupled with boundary-aware contrastive learning (BACL). Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets. The results demonstrate that our proposed approach can extract more domain-invariant features, minimizing over-segmentation and capturing more complete aneurysm structures.

翻译：摘要：脑动脉瘤的自动分割对于精确诊断和治疗规划至关重要。面对来自不同医疗机构的3D旋转血管造影（3DRA）数据中显著的域偏移与类别不平衡问题，该任务颇具挑战性。这些偏移包括图像外观、强度分布、分辨率和动脉瘤尺寸的差异，均使分割过程复杂化。为解决上述问题，我们提出了一种新颖的域泛化策略，该策略采用梯度手术指数移动平均（GS-EMA）优化技术，并结合边界感知对比学习（BACL）。本方法的独特性在于通过学习域不变特征，能够适应未见过的全新域，从而提升动脉瘤分割在多样化临床数据集上的鲁棒性与准确性。结果表明，所提方法能提取更丰富的域不变特征，减少过度分割，并捕获更完整的动脉瘤结构。