Efficient Conformal Volumetry for Template-Based Segmentation

2026-02-28

AI Summary

Existing template-based segmentation methods suffer from inefficiency and overly conservative confidence intervals in volumetric uncertainty quantification, primarily due to treating the registration process as a black box or relying on unavailable model features. This work proposes ConVOLT, a novel framework that, for the first time, integrates deformation field characteristics from deformable registration into conformal prediction calibration. By learning volume scaling factors directly in the deformation space, ConVOLT enables precise calibration of segmentation volume metrics. The approach overcomes the limitations of traditional black-box treatment in output space, consistently achieving target coverage while producing significantly tighter volume confidence intervals across multiple datasets and registration algorithms, thereby substantially improving both the efficiency and accuracy of uncertainty quantification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Template-based segmentation, a widely used paradigm in medical imaging, propagates anatomical labels via deformable registration from a labeled atlas to a target image, and is often used to compute volumetric biomarkers for downstream decision-making. While conformal prediction (CP) provides finite-sample valid intervals for scalar metrics, existing segmentation-based uncertainty quantification (UQ) approaches either rely on learned model features, often unavailable in classic template-based pipelines, or treat the registration process as a black box, resulting in overly conservative intervals when applied directly in output space. We introduce ConVOLT, a CP framework that achieves efficient volumetric UQ by conditioning calibration on properties of the estimated deformation field from template-based segmentation. ConVOLT calibrates a learned volumetric scaling factor from deformation space features. We evaluate ConVOLT on template-based segmentation tasks involving global, regional, and label volumetry across multiple datasets and registration methods. ConVOLT achieves target coverage while producing substantially tighter intervals than output-space conformal baselines. Our work paves way to exploit the registration process for efficient UQ in medical imaging pipelines.

Problem

• conformal prediction
• uncertainty quantification
• template-based segmentation
• volumetric biomarkers
• deformable registration

Innovation

• Conformal Prediction
• Template-based Segmentation
• Uncertainty Quantification
• Deformation Field
• Volumetric Biomarkers