Efficient Conformal Volumetry for Template-Based Segmentation

• URL: http://arxiv.org/abs/2603.00798v1
• Date: Sat, 28 Feb 2026 20:16:15 GMT
• Title: Efficient Conformal Volumetry for Template-Based Segmentation
• Abstract summary: We introduce ConVOLT, a framework that achieves efficient volumetric uncertainty quantification.<n>ConVOLT calibrates a learned volumetric scaling factor from deformation space features.<n>Our work paves way to exploit the registration process for efficient UQ in medical imaging pipelines.
• Score: 23.593891761167374
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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