Abstract
Accurate segmentation of the larynx is a fundamental preprocessing step in radiation therapy planning for head and neck (H&N) cancer. However, reliable laryngeal delineation from computed tomography (CT) remains challenging due to low soft-tissue contrast, morphological variation, and significant class imbalance. To address these limitations, this study presents an uncertainty-aware segmentation framework combining a SwinUNETR backbone with an S²Net-inspired multi-scale refinement head. By employing a memory-efficient Multi-Scale Feature Fusion (MSFF) module, the model enhances 3D encoder features to produce a coarse prediction. Crucially, a top-down Localization Calibration (LC) mechanism leverages voxel-wise uncertainty to refine this prediction, ensuring precise boundary definition in ambiguous regions without compromising anatomical integrity. The proposed method is validated on a private CT cohort comprising both healthy subjects and patients with laryngeal cancer, achieving Dice scores of 88.5% and 78.00%, respectively. Additionally, the framework is evaluated on the larynx subset of the publicly available SegRap2023 dataset, attaining a Dice score of 92.54%. Across both cohorts the framework performs on par with strong state-of-the-art baselines, with a statistically significant improvement on the cancer class, where boundary definition is most challenging. In clinically realistic radiation planning conditions, these results indicate consistent generalization performance.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_024.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{MenOlg_An_MICCAISAT2026,
author = { Menegaki, Olga AND Georgas, Konstantinos AND Matsopoulos, George K.},
title = { { An Uncertainty-Guided Multi-Scale SwinUNETR Framework for 3D Larynx Segmentation in CT } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}