Synergizing Radon transform and DINOv2 for artifact-resilient digital rock segmentation

Precise segmentation of core images acquired by X-ray computed tomography (X-CT) is fundamental for subsequent digital rock physics analysis. However, in cores with abundant high-density minerals, insufficient X-ray penetration often leads to distorted pore structures and severe artifacts, which compromise segmentation accuracy. To address this challenge, we propose a hybrid method that synergizes Radon transform with the DINOv2 vision foundation model. Our approach employs two stage strategy: first, a ‘hard correction’ via Radon transform to leverage the directional features of artifacts and suppress directional artifact patterns in the sinogram domain; second, a “soft learning” mechanism, where the fine-tuned DINOv2 model leverages its global semantic priors to discern and rectify residual artifact interference in the feature space. This hybrid approach not only suppresses artifact interference but also preserves the continuity and visual plausibility of pore structures. Experimental results demonstrate that, on synthetic artifact-contaminated samples, the model achieves incremental three-phase segmentation performance (mIoU = 0.755, F1 = 92.15%). While Appling to real core images, with heavy mineral artifacts, the model demonstrates robust error-correcting capabilities, yielding segmentations that align more closely with the expected rock microstructure than the original ground truth annotations. This study provides an effective and validated framework for artifact resilient digital rock segmentation, offering substantial improvements for quantitative DRP studies.