Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

arXiv:2311.16914v2 Announce Type: replace
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 …

arxiv brain brain imaging contrast cs.cv imaging type