Equivariant symmetry-aware head pose estimation for fetal MRI
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915900000567296 |
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| author | Muthukrishnan, Ramya Gagoski, Borjan Lee, Aryn Grant, P. Ellen Adalsteinsson, Elfar Billot, Benjamin Golland, Polina |
| author_facet | Muthukrishnan, Ramya Gagoski, Borjan Lee, Aryn Grant, P. Ellen Adalsteinsson, Elfar Billot, Benjamin Golland, Polina |
| contents | We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of diagnostic 2D MRI slices with 6-DoF head pose estimation, supported by rapid low-resolution 3D MRI volumes acquired before each 2D slice. Existing pose estimation methods struggle to generalize to clinical volumes due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, supporting future clinical translation. Our implementation is publicly available at github.com/MedicalVisionGroup/E3-Pose. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equivariant symmetry-aware head pose estimation for fetal MRI Muthukrishnan, Ramya Gagoski, Borjan Lee, Aryn Grant, P. Ellen Adalsteinsson, Elfar Billot, Benjamin Golland, Polina Computer Vision and Pattern Recognition We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of diagnostic 2D MRI slices with 6-DoF head pose estimation, supported by rapid low-resolution 3D MRI volumes acquired before each 2D slice. Existing pose estimation methods struggle to generalize to clinical volumes due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, supporting future clinical translation. Our implementation is publicly available at github.com/MedicalVisionGroup/E3-Pose. |
| title | Equivariant symmetry-aware head pose estimation for fetal MRI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04890 |