GS-EMA: Integrating Gradient Surgery Exponential Moving Average with
  Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in
  Aneurysm Segmentation
Fengming Lin Yan Xia Michael MacRaild Yash Deo Haoran Dou Qiongyao Liu Nina Cheng Nishant Ravikumar Alejandro F. Frangi
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.