Accurate segmentation of organs and tumors in CT and MRI scans is essential
for diagnosis, treatment planning, and disease monitoring. While deep learning
has advanced automated segmentation, most models remain task-specific, lacking
generalizability across modalities and institutions. Vision foundation models
(FMs) pretrained on billion-scale natural images offer powerful and
transferable representations. However, adapting them to medical imaging faces
two key challenges: (1) the ViT backbone of most foundation models still
underperform specialized CNNs on medical image segmentation, and (2) the large
domain gap between natural and medical images limits transferability. We
introduce \textbf{MedDINOv3}, a simple and effective framework for adapting
DINOv3 to medical segmentation. We first revisit plain ViTs and design a simple
and effective architecture with multi-scale token aggregation. Then, we perform
domain-adaptive pretraining on \textbf{CT-3M}, a curated collection of 3.87M
axial CT slices, using a multi-stage DINOv3 recipe to learn robust dense
features. MedDINOv3 matches or exceeds state-of-the-art performance across four
segmentation benchmarks, demonstrating the potential of vision foundation
models as unified backbones for medical image segmentation. The code is
available at https://github.com/ricklisz/MedDINOv3.