Faculty, Staff and Student Publications
Publication Date
1-1-2026
Journal
Medical Physics
DOI
10.1002/mp.70206
PMID
41423658
PMCID
PMC12719378
PubMedCentral® Posted Date
12-21-2025
PubMedCentral® Full Text Version
Post-print
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 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.
Keywords
Uncertainty, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Time Factors, Precision Medicine, Tomography, X-Ray Computed, Head and Neck Neoplasms, longitudinal medical imaging, personalized AI, semantic segmentation, test‐time optimization, uncertainty estimation
Published Open-Access
yes
Recommended Citation
Chun, Jaehee; Castelo, Austin; Woodland, McKell; et al., "Uncertainty-Guided Test-Time Optimization for Personalizing Segmentation Models in Longitudinal Medical Imaging" (2026). Faculty, Staff and Student Publications. 5384.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5384
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