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<p>Cone-beam computed tomography (CBCT)-based three-dimensional (3D) cephalometric analysis relies on accurate anatomical landmark identification, yet manual annotation is time-consuming and subject to inter- and intra-observer variability. While volumetric convolutional neural networks can improve accuracy, their computational and memory demands limit practical deployment. Multi-view consensus offers an efficient alternative by predicting per-view two-dimensional (2D) heatmaps and fusing them geometrically, but its performance can degrade when view-dependent uncertainty produces unreliable results. We propose a reliability-aware view-adaptive consensus framework for 3D cephalometric landmark identification from CBCT projection views. A shared-weight 2D network predicts per-view 2D heatmaps and landmark reliability scores, which adaptively modulate each view’s contribution in an end-to-end differentiable geometric consensus. This framework yields deterministic fusion without stochastic inlier sampling or multi-stage refinement. With 5-fold cross-validation, the proposed method achieved the lowest mean radial error (1.26&#xa0;<i>mm</i>; 95<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> CI [1.20, 1.33]) and the highest successful detection rate at 2&#xa0;<i>mm</i> (88.41<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation>), while maintaining low computational loads. Ablation studies further validated the key design choices and highlighted a favorable accuracy-efficiency trade-off with a limited number of views.</p>

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Reliability-Aware View-Adaptive Consensus for 3D Cephalometric Landmark Identification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Min-Hyuk Choi,
  • Jo-Eun Kim,
  • Kyung-Hoe Huh,
  • Sam-Sun Lee,
  • Min-Suk Heo,
  • Won-Jin Yi

摘要

Cone-beam computed tomography (CBCT)-based three-dimensional (3D) cephalometric analysis relies on accurate anatomical landmark identification, yet manual annotation is time-consuming and subject to inter- and intra-observer variability. While volumetric convolutional neural networks can improve accuracy, their computational and memory demands limit practical deployment. Multi-view consensus offers an efficient alternative by predicting per-view two-dimensional (2D) heatmaps and fusing them geometrically, but its performance can degrade when view-dependent uncertainty produces unreliable results. We propose a reliability-aware view-adaptive consensus framework for 3D cephalometric landmark identification from CBCT projection views. A shared-weight 2D network predicts per-view 2D heatmaps and landmark reliability scores, which adaptively modulate each view’s contribution in an end-to-end differentiable geometric consensus. This framework yields deterministic fusion without stochastic inlier sampling or multi-stage refinement. With 5-fold cross-validation, the proposed method achieved the lowest mean radial error (1.26 mm; 95 \(\%\) % CI [1.20, 1.33]) and the highest successful detection rate at 2 mm (88.41 \(\%\) % ), while maintaining low computational loads. Ablation studies further validated the key design choices and highlighted a favorable accuracy-efficiency trade-off with a limited number of views.