A low-rank representation for unsupervised registration of medical images
This work addresses the challenge of noisy medical image registration for healthcare applications, offering an incremental improvement over existing unsupervised methods.
The paper tackles the problem of unsupervised medical image registration under noisy conditions by proposing Regnet-LRR, a method that projects noisy images into a noise-free low-rank space for similarity computation, resulting in improved generalization and robustness with significant gains in noisy data scenarios.
Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these methods become less effective when the image quality varies, e.g., some images are corrupted by substantial noise or artifacts. In this work, we propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem. We project noisy images into a noise-free low-rank space, and then compute the similarity between the images. Based on the low-rank similarity measure, we train the registration network to predict the dense deformation fields of noisy image pairs. We highlight that the low-rank projection is reformulated in a way that the registration network can successfully update gradients. With two tasks, i.e., cardiac and abdominal intra-modality registration, we demonstrate that the low-rank representation can boost the generalization ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.