[2603.00798] Efficient Conformal Volumetry for Template-Based Segmentation

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.00798 (eess) [Submitted on 28 Feb 2026] Title:Efficient Conformal Volumetry for Template-Based Segmentation Authors:Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan View a PDF of the paper titled Efficient Conformal Volumetry for Template-Based Segmentation, by Matt Y. Cheung and 2 other authors View PDF HTML (experimental) 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. Con...