ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance
This work addresses a specific bottleneck in brain lesion segmentation for medical imaging, offering an incremental improvement in data augmentation techniques.
The paper tackles the problem of intensity disparities and boundary artifacts in synthetic brain lesion MRI images used for data augmentation, which weaken segmentation performance, by proposing ARHNet, an adaptive region harmonization framework that improves segmentation results on the ATLAS 2.0 dataset.
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.