Surgical decisions are informed by aligning rapid portable 2D intraoperative
images (e.g., X-rays) to a high-fidelity 3D preoperative reference scan (e.g.,
CT). 2D/3D image registration often fails in practice: conventional
optimization methods are prohibitively slow and susceptible to local minima,
while neural networks trained on small datasets fail on new patients or require
impractical landmark supervision. We present DiffPose, a self-supervised
approach that leverages patient-specific simulation and differentiable
physics-based rendering to achieve accurate 2D/3D registration without relying
on manually labeled data. Preoperatively, a CNN is trained to regress the pose
of a randomly oriented synthetic X-ray rendered from the preoperative CT. The
CNN then initializes rapid intraoperative test-time optimization that uses the
differentiable X-ray renderer to refine the solution. Our work further proposes
several geometrically principled methods for sampling camera poses from
$\mathbf{SE}(3)$, for sparse differentiable rendering, and for driving
registration in the tangent space $\mathfrak{se}(3)$ with geodesic and
multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy
across surgical datasets at intraoperative speeds, improving upon existing
unsupervised methods by an order of magnitude and even outperforming supervised
baselines. Our code is available at this https URL

通过 2D/3D 图像配准，利用不依赖手动标注数据的自监督方法和可微分物理渲染，实现了精确的手术决策，提高了手术速度，并超越了有监督基线模型。