Enhancing CNN regressors with contour encoding and self-supervision for improved 3D/2D x-ray to CT registration in...

With advances in deep learning, regression-based methods have shown promising results in 3D/2D medical image registration. However, strict intraoperative radiation dose constraints produce low-dose x-ray images with severe blur and reduced contrast, significantly degrading registration accuracy and limiting precise image-guided spinal interventions. We propose the Contour Encoding Regressor (CER), a novel end-to-end CNN framework that extracts highly discriminative features directly from binary contour masks of intraoperative x-rays without any restrictions on contour length, shape, or morphology. These contour features are efficiently encoded by a dedicated module and seamlessly fused into the regression pipeline to improve robustness against image degradation. To further enhance pose estimation, CER employs a dual-branch architecture that explicitly decouples rotational and translational parameters, thereby reducing mutual interference and improving overall accuracy. In addition, a self-supervised fine-tuning strategy with a tailored multi-component loss function is introduced to adapt the model to blurred low-dose conditions and minimize residual errors. On low-dose x-ray images, CER achieves a mean target registration error of 1.39 mm-a clinically acceptable threshold-while outperforming state-of-the-art methods in accuracy and enabling real-time performance (0.03-0.06 s per frame on clinically accessible GPUs). These improvements meet the stringent precision and speed requirements of intraoperative navigation, offering strong potential to enhance surgical safety and outcomes in minimally invasive spinal procedures.