Uncertainty-guided test-time optimization for personalizing segmentation models in longitudinal medical imaging.

ABSTRACT:

Background

Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

Purpose

We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

Methods

Our framework personalizes a generalized segmentation model us

SUBMITTER: Chun J 

PROVIDER: S-EPMC12719378 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature