[2605.03098] One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.03098 (cs)
[Submitted on 4 May 2026]
Title:One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Authors:Nathan Molinier, Hendrik Möller, Thomas Dagonneau, Anna Curto-Vilalta, Robert Graf, Matan Atad, Daniel Rueckert, Jan S. Kirschke, Julien Cohen-Adad
View a PDF of the paper titled One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation, by Nathan Molinier and Hendrik M\"oller and Thomas Dagonneau and Anna Curto-Vilalta and Robert Graf and Matan Atad and Daniel Rueckert and Jan S. Kirschke and Julien Cohen-Adad
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Abstract:Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. However, model performance remains limited by the scarcity of high-quality annotated data and insufficient generalization across imaging protocols. This limitation is particularly evident in MRI and CT, where models are typically trained on a single acquisition sequence and exhibit reduced robustness when applied to unseen sequences or contrasts. Although data augmentation is widely used to improve general robustness on medical images, its impact on cross-modality generalization has not been quantitatively explored. In this work, we study a targeted set of data augmentation techniques designed to improve cross-modality transfer. We train three spine segmentation models, each on a single-modality/sequence dataset, and evaluate them across seven out-of-distribution datasets (spanning CT and MRI), reflecting a realistic single-sequence training and multi-sequence/contrast/modality deployment scenario. Our results demonstrate substantial performance gains on unseen domains (average Dice gain of 155 %) while preserving in-domain accuracy (average Dice decrease of 0.008 %), including effective transfer between CT and MRI. To mitigate the computational cost typically associated with strong data augmentation, we implement GPU-optimized augmentations that maintain, and even improve, training efficiency by approximately 10 %. We release our approach as an open-source toolbox, enabling seamless integration into commonly used frameworks such as nnUNet and MONAI. These augmentations significantly enhance robustness to heterogeneous clinical imaging scenarios without compromising training speed.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.03098 [cs.CV]
(or arXiv:2605.03098v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.03098
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hendrik Möller [view email] [v1] Mon, 4 May 2026 19:26:29 UTC (11,180 KB)
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