Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models

Purpose To evaluate random convolutions as an augmentation strategy for improving domain generalization of deep learning-based segmentation models in medical imaging. Materials and Methods In this retrospective study, a random-convolution-based augmentation strategy was applied to abdominal organ segmentation (AbdomenCT-1k: 361 CT images; AMOS: 298 CT and 59 MRI scans) and brain tissue segmentation (IXI: 504 T1-weighted [T1w] images from Guy’s and Hammersmith Hospitals, 146 paired T1w/T2-weighted [T2w] images from the Institute of Psychiatry). Performance was compared with baseline and state-of-the-art segmentation models (TotalSegmentator and deepAtropos). Random convolution configurations were analyzed for effects on in-and out-of-domain performance. Results The random convolution-enhanced UNet achieved in-domain Dice scores comparable to state-of-the-art baselines (CT: 0.93 vs TotalSegmentator: 0.95; T1w imaging: 0.83 vs deepAtropos: 0.79). Out-of-domain Dice scores were significantly higher (MRI: 0.93, T2w imaging: 0.52) compared with baselines (TotalSegmentator in MRI: 0.85, deepAtropos in T2w imaging: 0.33, FDR-adjusted P values < 0.001). Augmentation probability and configuration influenced the trade-off between in-and out-of-domain performance. Conclusion Random convolutions yielding more robust segmentation models that generalized better to unseen domains than models trained without random convolutions and are compatible with diverse segmentation architectures. ©RSNA, 2025