Liu, P;
Puonti, O;
Hu, X;
Alexander, DC;
Iglesias, JE;
(2025)
Brain-ID: Learning Contrast-Agnostic Anatomical Representations for Brain Imaging.
In:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
(pp. pp. 322-340).
Springer Nature
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Abstract
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT). Yet, they struggle to generalize in uncalibrated modalities – notably magnetic resonance (MR) imaging, where performance is highly sensitive to the differences in MR contrast, resolution, and orientation. This prevents broad applicability to diverse real-world clinical protocols. We introduce Brain-ID, an anatomical representation learning model for brain imaging. With the proposed “mild-to-severe” intra-subject generation, Brain-ID is robust to the subject-specific brain anatomy regardless of the appearance of acquired images. Trained entirely on synthetic inputs, Brain-ID readily adapts to various downstream tasks through one layer. We present new metrics to validate the intra/inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks (Fig. 1). Extensive experiments on six public datasets demonstrate that Brain-ID achieves state-of-the-art performance in all tasks on different MR contrasts and CT, and more importantly, preserves its performance on low-resolution and small datasets. Code is available at https://github.com/peirong26/Brain-ID.
| Type: | Proceedings paper |
|---|---|
| Title: | Brain-ID: Learning Contrast-Agnostic Anatomical Representations for Brain Imaging |
| Event: | Computer Vision – ECCV 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-73254-6_19 |
| Publisher version: | https://doi.org/10.1007/978-3-031-73254-6_19 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204234 |
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