  Accurately segmenting brain lesions in MRI scans is critical for providing
patients with prognoses and neurological monitoring. However, the performance
of CNN-based segmentation methods is constrained by the limited training set
size. Advanced data augmentation is an effective strategy to improve the
model's robustness. However, they often introduce intensity disparities between
foreground and background areas and boundary artifacts, which weakens the
effectiveness of such strategies. In this paper, we propose a foreground
harmonization framework (ARHNet) to tackle intensity disparities and make
synthetic images look more realistic. In particular, we propose an Adaptive
Region Harmonization (ARH) module to dynamically align foreground feature maps
to the background with an attention mechanism. We demonstrate the efficacy of
our method in improving the segmentation performance using real and synthetic
images. Experimental results on the ATLAS 2.0 dataset show that ARHNet
outperforms other methods for image harmonization tasks, and boosts the
down-stream segmentation performance. Our code is publicly available at
https://github.com/King-HAW/ARHNet.
