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

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 (1.72 mm), and (4) only anatomically correct, image-specific priors yield 1.04 mm -- functioning as a training-time regularizer. No prior generation is needed at deployment. Five-fold cross-validation (p=0.0015), patient-level permutation testing (p<0.0001, n=151), reproduced baselines, Grad-CAM analysis, and clinical validation (100% skeletal classification across 151 patients including 72 boundary cases, kappa=1.00) provide converging evidence. Cross-domain experiments support the hypothesis that prior effectiveness depends on landmark spatial entropy -- confirmed prospectively across four domains. Supplementary materials included.