\begin{abstract}
% Landmark-based 2D/3D pelvis registration using fluoroscopy images rely on accurately detected anatomical landmarks, but current learning-based methods treat all predicted landmarks as equally reliable. Erroneous or occluded landmarks can therefore dominate the pose solver and negatively influence the surgical guidance. We propose an uncertainty-aware landmark selection framework that integrates per-landmark epistemic uncertainty into a standard landmark-based 2D/3D registration pipeline. A patient-held-out U-Net is trained to predict the 14 pelvic anatomical landmarks on C-arm fluoroscopy image. At test time, decoder dropout remains active and Monte Carlo sampling is used to obtain multiple stochastic predictions per landmark. We define an uncertainty score as the deviation of these samples around their mean and rank landmarks on each image. For pose estimation, we compare a baseline that uses all visible landmarks against a variant that discard the $K$ most uncertain landmarks before non-linear perspective projection-based registration. Experiments on held-out patient's pelvic fluorosocpy image demonstrates that uncertainty-aware landmark selection can reduce the accuracy of landmark prediction and rotational and translational pose errors.
% This paper presents a landmark-based 2D/3D pelvis registration framework that explicitly accounts for the reliability of each detected landmark using uncertainty-aware landmark selection. We consider C-arm fluoroscopy images where a U-Net predicts fourteen pelvic anatomical landmarks. To model epistemic uncertainty, decoder dropout is kept active at test time and multiple stochastic forward passes are performed, creating Monte Carlo samples of each landmark position. From these samples, we derive a per-landmark uncertainty score based on the radial dispersion around the mean prediction and integrate this score into a standard perspective projection and non-linear least-sqaure registration pipeline. The pelvic pose is then estimated either from all visible landmarks or from a subset obtained by discarding the most uncertain landmarks on each image. This approach enables automatic suppression of outlier landmarks and improves numerical stability of the pose solver. Experimental results on a held-out patient set of fluoro images demonstrate increased accuracy in landmark detection and rotational and translational registration compared with using all landmarks, offering a simple and computationally efficient step toward more trustworthy image-guided orthopaedic procedures. Code: [redacted for review]
% This paper presents a novel method for landmark-based 2D/3D pelvis registration method that selects landmarks according to their predicted reliability using uncertainty-aware landmark selection process. Our method shows that landmarks with high uncertainty estimated from Monte Carlo dropout in a U-Net landmark detector, are more likely to be inaccurate and degrade registration performance. Instead of using all visible landmarks, we rank the 2D pelvis landmarks on each fluoroscopy image by their uncertainty and discard the most unreliable points before solving for the 3D pelvis pose estimation with a perspective projection-based non-linear optimizer. With the finding that removing a small subset of highly uncertain landmarks improves both landmark prediction accuracy and rotation and translation accuracy, this allows landmark-based registration methods to remain computationally efficient while becoming more robust to outliers and occlusions. Experimental results on held-out patient's pelvic fluoroscopy images demonstrate that the proposed uncertainty-aware landmark selection leads to higher registration accuracy with minimal additional computational cost, offering improved precision and applicability in surgical and clinical settings. 
% In this paper, we study whether uncertainty-aware landmark selection can improve landmark-based 2D/3D pelvis registration. In fluoroscopy-guided pelvic procedures, landmark-based registration is efficient and interpretable, but implicitly assumes all automatically detected landmarks are equally reliable. However, in practice, outliers and occlusions can severely degrade pose estimation. Using an oracle experiment with ground truth 2D landmarks, we first show that discarding landmarks with the largest prediction errors consistently reduces both rotation and translation estimation errors, revealing a strong link between landmark accuracy and registration quality. Motivated by this, we estimate per-landmark epistemic uncertainty via Monte Carlo dropout in a U-Net detector. This method ranks landmarks on each image by uncertainty and discard the most uncertain points before solving for the 3D pelvic pose with a perspective projection-based non-linear optimizer. On a public pelvic CT dataset under a patient-held-out evaluation, this uncertainty-aware selection leads to lower 2D detection error and 3D pose error than using all visible landmarks, with negligible additional computation.
% In this work, we investigate whether uncertainty-aware landmark modeling can improve both training and inference for landmark-based 2D/3D pelvis registration. In fluoroscopy-guided pelvic procedures, registration via automatically detected anatomical landmarks is efficient and geometrically interpretable. However, standard pipelines implicitly treat all landmarks as equally reliable, so outliers and occlusions can severely bias pose estimation. We propose a Monte Carlo dropout based framework that estimates per-landmark epistemic uncertainty and uses it in two complementary ways. First, during fine-tuning of a pretrained landmark detection model, we use this uncertainty to weight each landmark in a differentiable pose estimation loss, allowing the network to modulate the influence of unreliable correspondences. Second, at test time, the same uncertainty estimates are used to weight or discard landmarks before estimating the pelvic pose. On a public pelvic CT dataset under a patient-held-out evaluation, we empirically show that our uncertainty-aware weighting scheme reduces 3D pelvic pose estimation error compared to uncertainty-agnostic baselines.
Landmark-based 2D/3D pelvis registration is vulnerable to noisy or ambiguous landmark detections in fluoroscopy, which can destabilize downstream pose estimation. We present an uncertainty-aware registration framework that models epistemic uncertainty in predicted landmarks and incorporates it directly into the Perspective-n-Point formulation. Using Monte Carlo dropout within a U-Net detector, we compute sample-specific per-landmark reliability estimates using the variance of multiple stochastic forward passes. These reliability estimates guide two complementary strategies: continuous weighting, which integrates uncertainty into a weighted PnP optimization, and discrete selection, which removes the most uncertain landmarks during inference. We evaluate the framework on both CT-derived synthetic fluoroscopy and real fluoroscopy from DeepFluoro. Our experiments show that uncertainty provides a principled mechanism for identifying unreliable landmarks and stabilizing pose estimation, enabling more robust registration and establishing a foundation for uncertainty-guided image-guided surgical workflows. Code: \url{https://github.com/yehyunsuh/Uncertainty-Aware-Pelvic-Pose-Estimation}

% We evaluate the framework on synthetic fluoroscopy derived from a public pelvic CT dataset. .
\end{abstract}

\begin{keywords}
% test-time Bayesian uncertainty estimation, uncertainty heatmap visualization, landmark variance ellipse, per-landmark deviation, per-image uncertainty score
% Landmark-based 2D/3D Pelvis Registration, Uncertainty-Aware Landmark Detection, Weighted Pose Estimation, Monte Carlo Dropout, Epistemic Uncertainty
Uncertainty-Weighted Pose Estimation, Landmark-based 2D/3D Registration, Monte Carlo Dropout, Epistemic Uncertainty Modeling
\end{keywords}
