ArticleIEEE transactions on medical imaging2024
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- NeuroLangSeg: Language-Guided Subcortical Segmentation with Pseudo-Supervision and Anatomical-Linguistic Validation.Proceedings of machine learning research · 2026Article
- Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Towards automatic US-MR fetal brain image registration with learning-based methods.NeuroImage · 2025Article
- Network conditioning for synergistic learning on partial annotations.Proceedings of machine learning research · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet, CNNs do not exploit natural symmetries in this task, as they are equivariant to translations (their outputs shift with their inputs) but not to rotations. Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking. While steerable E-CNNs can extract corresponding features across different poses, testing them on noisy medical images reveals that they do not have enough learning capacity to learn noise invariance. Thus, we introduce a hybrid architecture that pairs a denoiser with an E-CNN to decouple the processing of anatomically irrelevant intensity features from the extraction of equivariant spatial features. Rigid transforms are then estimated in closed-form. EquiTrack outperforms state-of-the-art learning and optimisation methods for motion tracking in adult brain MRI and fetal MRI time series. Our code is available at https://github.com/BBillot/EquiTrack.
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Registered trials
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