Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection 文章

arXiv:2605.03358v2 Announce Type: replace Abstract: Clinicians trace cephalometric radiographs by following a structured anatomical workflow -- yet no prior system explicitly encodes this into computation. We present a five-phase anatomy-guided pipeline producing confidence-weighted spatial priors that shape HRNet-W32 training. The system achieves 1.04 mm mean radial error on 25 landmarks across 1,502 radiographs from 7+ imaging devices -- comparable to HYATT-Net (1.05 mm on CEPHA29) via explicit anatomical priors rather than learned attention. A three-way ablation isolates the mechanism: anatomical priors maintain a 1% validation-to-test gap, while removing priors yields an 88% gap (1.94 mm) -- despite identical validation convergence. A training x inference prior matrix confirms that (1) all models are inference-independent, (2) the 28-channel architecture alone provides no benefit, (3) random priors are partial and unstable (1.