Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
This addresses a foundational challenge in medical image analysis and image-guided radiotherapy by improving registration accuracy across modalities, though it appears incremental as it builds on existing representation learning methods.
The paper tackled the problem of establishing dense anatomical correspondence across distinct imaging modalities in medical image registration, proposing a modality-agnostic structural representation learning method that outperformed conventional approaches in discriminability and accuracy on multiphase CT, abdomen MR-CT, and brain MR T1w-T2w registration.
Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However, the former is sensitive to locally varying noise, while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans, causing ambiguity in determining the anatomical correspondence across scans with different modalities. In this paper, we propose a modality-agnostic structural representation learning method, which leverages Deep Neighbourhood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT, abdomen MR-CT, and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.