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

• URL: http://arxiv.org/abs/2311.16914v2
• Date: Sun, 10 Mar 2024 14:35:30 GMT
• Title: Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
• Authors: Peirong Liu and Oula Puonti and Xiaoling Hu and Daniel C. Alexander and Juan E. Iglesias
• Abstract summary: We introduce Brain-ID, an anatomical representation learning model for brain imaging. With the proposed "mild-to-severe" intrasubject generation, Brain-ID is robust to the subject-specific brain anatomy. We present new metrics to validate the intra- and inter-subject robustness, and evaluate their performance on four downstream applications.
• Score: 11.06907516321673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (e.g., contrast, deformation, resolution, artifacts). Trained entirely on synthetic data, Brain-ID readily adapts to various downstream tasks through only one layer. We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks. Extensive experiments on six public datasets demonstrate that Brain-ID achieves state-of-the-art performance in all tasks on different MRI modalities and CT, and more importantly, preserves its performance on low-resolution and small datasets. Code is available at https://github.com/peirong26/Brain-ID.

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