Uncertainty quantification for detecting out-of-distribution cases in deep learning segmentation of head and neck organs of interest.

Authors

Joëlle E van Aalst, Rita Simões, Federica C Maruccio, Daniel C MacRae, Luuk van der Hoek, Johannes A Langendijk, Peter M A van Ooijen, Tomas M Janssen, Charlotte L Brouwer

Published in

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. Pages 111826. Oct 13, 2026. Epub Oct 13, 2026.

Abstract

Quality assurance (QA) of auto-segmentations is necessary for safe clinical deployment. Deep learning models are optimised for data similar to their training set, but reliability drops with out-of-distribution (OOD) inputs. This study evaluates whether uncertainty quantification (UQ) can detect OOD inputs arising from distribution shift in head and neck organ of interest (OOI) auto-segmentation.
Monte Carlo dropout-based uncertainty scores and geometric accuracy of nnU-Net models were evaluated in two experiments. First, as a proof-of-concept, we assessed whether uncertainty scores detect metal artefact (N = 10) from non-artefact cases (N = 10) on a baseline model (mixed training data, N = 620) and a no-metal model (artefact-free training data, N = 620). Second, in a clinically realistic experiment, we split a fine-tuning dataset by uncertainty score to obtain a narrow model (low-uncertainty cases, N = 21) and a broad model (high-uncertainty cases, N = 21). A held-out test set (N = 76) evaluated whether segmentation accuracy differed between uncertainty categories.
Both experiments showed that uncertainty scores reflect the learned training distribution. In the supervised experiment, the no-metal model demonstrated greater uncertainty score separation between artefact and non-artefact cases (1.1 × 10⁵ vs 0.6 × 10⁵) than the baseline model (1.1 × 10⁵ vs 0.8 × 10⁵). In the unsupervised experiment, the narrow model performed significantly better on certain than uncertain cases (p = 0.04), while no difference was seen for the broad model (p = 0.34), which did include these cases in training.
Uncertainty quantification can detect distribution shifts in radiotherapy auto-segmentation. This could support automated QA workflows by identifying in-distribution cases and enabling the application of specialised models.

PMID:
42869247
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

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