Conference proceeding
Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration
Proceedings (International Symposium on Biomedical Imaging), pp.1-5
04/14/2025
DOI: 10.1109/ISBI60581.2025.10981173
Abstract
Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeo-morphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for model uncertainty, which can be useful for improving performance. We propose an uncertainty-aware test-time adaptation framework for inverse consistent diffeomorphic lung registration. Our method uses Monte Carlo (MC) dropout to estimate spatial uncertainty that is used to improve model performance. We train and evaluate our method for inspiratory-to-expiratory CT registration on a large cohort of 675 subjects from the COPDGene study, achieving a higher Dice similarity coefficient (DSC) between the lung boundaries (0.966) compared to both VoxelMorph (0.953) and TransMorph (0.953). Our method demonstrates consistent improvements in the inverse registration direction as well with an overall DSC of 0.966, higher than VoxelMorph (0.958) and TransMorph (0.956). Paired t-tests indicate statistically significant improvements.
Details
- Title: Subtitle
- Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration
- Creators
- Muhammad F.A. Chaudhary - University of Alabama at BirminghamStephanie M. Aguilera - University of Alabama at BirminghamArie Nakhmani - University of Alabama at BirminghamJoseph M. Reinhardt - University of IowaSurya P. Bhatt - University of Alabama at BirminghamSandeep Bodduluri - University of Alabama at Birmingham
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings (International Symposium on Biomedical Imaging), pp.1-5
- Publisher
- IEEE
- DOI
- 10.1109/ISBI60581.2025.10981173
- eISSN
- 1945-8452
- Number of pages
- 5
- Language
- English
- Date published
- 04/14/2025
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984824228702771
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