Abstract: Objective To address the scarcity of annotated data for pediatric abdominal CT imaging and the insufficient generalization capability of existing models,we constructed a self-supervised pretraining architecture tailored for pediatric CT domain adaptation based on the visual foundation model DINOv3,and validated its performance in the task of pediatric abdominal multi-organ segmentation. Methods We built a general-purpose radiological visual representation using the large-scale adult CT dataset CT-3M,and introduced a Gram-anchoring mechanism that employs a frozen adult pretrained model as a structural teacher to guide domain alignment of local topological structures on unlabeled pediatric CT data. Combined with a multi-scale feature aggregation strategy and a lightweight Primus decoder,downstream segmentation tasks were evaluated on a public pediatric CT dataset. Based on case-wise paired results,we compared the mean Dice similarity coefficient (DSC) and mean intersection over union (IoU) between our model and the baseline nnU-Net using the Wilcoxon signed-rank test,and computed the relative performance improvements. Results A total of 867 abdominal CT imaging cases were collected,constituting a pretraining dataset comprising 367 588 two-dimensional CT slices. On the public Pediatric-CT-SEG dataset (359 cases),our model achieved a mean DSC of (71.38± 1.08)% and a mean IoU of (63.73±1.01)%,representing improvements of 3.22% and 3.59% over the baseline nnU-Net,respectively,with statistically significant differences (P<0.05). Stable improvements in mean DSC were also observed for small-volume or boundary-ambiguous organs,including the duodenum (5.78%),pancreas (4.69%),left adrenal gland (2.89%),right adrenal gland (1.12%),and gallblad-der (1.86%). Ablation experiments demonstrated that DSC improved by 1.44%,3.66%,5.78%,and 6.45% following adult pretraining,pediatric domain adaptation,high-resolution adaptation,and multi-scale feature aggregation,respectively. Conclusions The self-supervised pretraining framework proposed in this study effectively alleviates the domain shift between adult and pediatric abdominal CT images,significantly enhances segmentation accuracy for pediatric abdominal multi-organs-particularly small organs and structures with complex boundaries-and provides a reliable technical solution for intelligent pediatric imaging analysis in scenarios with limited annotated data.