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

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 using patient-specific prior imaging and selects the optimal checkpoint based on the minimum voxel-wise predictive uncertainty, estimated via Monte Carlo Dropout (TTO-MCD) or Deep Ensembling (TTO-DE). We evaluated the approach on three datasets: 214 pancreas (CT) scans, 243 liver (CT) scans, and 175 head-and-neck tumor (MRI) scans, each containing a subset of patients with paired longitudinal scans to enable patient-specific personalization. Each patient's follow-up scan was held out for testing. As a baseline, we implemented a fixed-epoch personalization strategy (Pre-TTO) using a fivefold cross-test design to emulate deployable model selection without test label leakage. RESULTS: TTO methods consistently outperformed the Pre-TTO and unpersonalized baseline across standard metrics, including the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), Mean Surface Distance (MSD), and the proposed LogPenalty Score (LPS), which provides a bounded, interpretable scale that jointly reflects volumetric and boundary fidelity. Paired t-tests confirmed statistically significant improvements for pancreas and liver datasets (p  <  0.05), while favorable trends were observed in the head-and-neck dataset despite greater anatomical variability. Both TTO-MCD and TTO-DE achieved near-optimal performance without requiring access to labels at test time. CONCLUSION: Uncertainty-guided TTO provides a robust, validation-free strategy for optimizing patient-specific segmentation models in longitudinal medical imaging. By tailoring personalization based on predictive uncertainty, our method improves segmentation quality across a range of imaging modalities and anatomical targets. This framework supports broad clinical deployment of personalized AI and motivates future extensions to contextual integration and multi-label segmentation. Code is publicly available at https://github.com/jchun-ai/uncertainty-tto.