Frozen DINOv3 features injected into a 3D U-Net via Room-Lite mixing and calibrated fusion reach Dice 0.758 for 3DRA aneurysm segmentation and remove all cross-dataset failures
The work proposes DINO-3DRA, a dual-path framework that injects frozen 2D vision foundation model DINOv3-Small features into a trainable 3D U-Net backbone through Room-Lite spatial mixing and calibrated residual fusion, achieving state-of-the-art aneurysm segmentation on multi-centre 3D rotational angiography (3DRA) @neurIST data (233 patients, four institutions) with Dice 0.758, HD95 2.75 mm and a 13% gain over nnU-Net using only 5.72M trainable parameters; ablations attribute the gains to structured cross-dimensional transfer rather than loss design, and without fine-tuning on CADA (n=45) and SHINY-ICARUS (n=30) it reduces Dice<0.5 failures from 11.1% and 3.3% to 0%.