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 dataset: 361 CT images; Abdominal Multi Organ Segmentation [AMOS] dataset: 298 CT and 59 MRI scans) and brain tissue segmentation (Information eXtraction from Images [IXI] dataset: 504 T1-weighted images from Guy's and Hammersmith Hospitals, 146 paired T1-weighted and T2-weighted 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 U-Net achieved in-domain Dice scores comparable to state-of-the-art baselines (CT: 0.93 vs TotalSegmentator: 0.95; T1-weighted imaging: 0.83 vs deepAtropos: 0.79). Out-of-domain Dice scores were significantly higher (MRI: 0.93, T2-weighted imaging: 0.52) compared with baselines (TotalSegmentator in MRI: 0.85, deepAtropos in T2-weighted imaging: 0.33; false discovery rate-adjusted P < .001). Augmentation probability and configuration influenced the trade-off between in- and out-of-domain performance. Conclusion Random convolutions yielding more robust segmentation models generalized better to unseen domains than models trained without random convolutions and are compatible with diverse segmentation architectures. Keywords: MR-Imaging, CT, Supervised Learning, Segmentation, Abdomen/GI, Experimental Investigations Supplemental material is available for this article. © RSNA, 2025 See also commentary by Mathai in this issue.
Keywords: Abdomen/GI; CT; Experimental Investigations; MR-Imaging; Segmentation; Supervised Learning.