Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation
This work addresses the challenge of inconsistent brain surface alignments for researchers in neuroimaging, though it is incremental as it builds on existing adversarial training techniques.
The paper tackled the problem of brain surface segmentation across misaligned cortical geometries by using adversarial training for graph domain adaptation, achieving an 8% mean improvement in performance over non-adversarial methods on the MindBoggle dataset.
The varying cortical geometry of the brain creates numerous challenges for its analysis. Recent developments have enabled learning surface data directly across multiple brain surfaces via graph convolutions on cortical data. However, current graph learning algorithms do fail when brain surface data are misaligned across subjects, thereby affecting their ability to deal with data from multiple domains. Adversarial training is widely used for domain adaptation to improve the segmentation performance across domains. In this paper, adversarial training is exploited to learn surface data across inconsistent graph alignments. This novel approach comprises a segmentator that uses a set of graph convolution layers to enable parcellation directly across brain surfaces in a source domain, and a discriminator that predicts a graph domain from segmentations. More precisely, the proposed adversarial network learns to generalize a parcellation across both, source and target domains. We demonstrate an 8% mean improvement in performance over a non-adversarial training strategy applied on multiple target domains extracted from MindBoggle, the largest publicly available manually-labeled brain surface dataset.