Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels

•Anomaly detection-inspired few-shot medical image segmentation model.•Supervoxel-based self-supervised learning to exploit 3D image nature.•Evaluated on representative datasets for organ and cardiac segmentation.•Increased robustness to over-segmentation by only modeling the foreground.