Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach (1703.10908v4)

Abstract: This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.

• The paper introduces a novel method that leverages momentum parameterization in LDDMM to predict deformation fields swiftly and accurately.
• It combines a sliding window patch strategy with Bayesian uncertainty estimation and a correction network to enhance registration performance.
• Experimental results on multiple datasets show that Quicksilver achieves state-of-the-art accuracy while significantly reducing computation time.

An Evaluation of "Quicksilver: Fast Predictive Image Registration — a Deep Learning Approach"

The paper introduces Quicksilver, a method for rapidly executing deformable image registration by predicting deformation parameters using deep learning. The proposed technique primarily addresses the computation-intensive nature of traditional registration methods. The central innovation of Quicksilver lies in predicting the momentum parameterization of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model, which facilitates patch-wise registration while retaining LDDMM's advantageous properties such as guaranteeing diffeomorphic mappings.

Methodology and Contributions

1. Momentum Parameterization: Quicksilver employs the momentum-parameterization of LDDMM for its predictions. This approach benefits from compact support around image edges, which allows for efficient patch-wise predictions and ensures smooth velocity fields post-smoothing. The method maintains the theoretical guarantees of LDDMM by retaining regularization properties, ensuring diffeomorphic transformations.
2. Fast Computation: By using a sliding window strategy with patch pruning and large stride values, the number of predicted patches is reduced significantly. This results in a drastic speed-up in prediction computation while preserving registration accuracy.
3. Bayesian Approaches: The probabilistic version of the network quantifies the uncertainty of predicted deformations, providing a valuable tool for analysis in medical imaging tasks.
4. Correction Network: The paper introduces a correction network to refine the results of the initial prediction network, enhancing prediction accuracy by applying momentum corrections.
5. Multi-modal Registration: Quicksilver demonstrates capability for multi-modal registration, revealing potential for diverse imaging modality compatibility.

Experimental Findings

Quicksilver's performance was evaluated using multiple datasets, including the OASIS and IBIS datasets for atlas-to-image and image-to-image registration, respectively. It demonstrated competitive registration accuracies comparable to numerical optimization but with substantial reductions in computation time. Specifically, experiments show that Quicksilver attains state-of-the-art results on four validation datasets, making it a viable and efficient alternative to traditional methods.

The statistical analysis supports the method's efficacy, as both prediction and correction techniques effectively reduce difference errors from baseline registration approaches. The use of a correction network significantly improved the accuracy of predictions.

Implications and Future Directions

Quicksilver's introduction marks a notable development in the field of image registration, particularly in medical imaging contexts requiring fast and accurate processing. The method's ability to perform accurate deformation predictions quickly aligns well with the growing demand for processing large-scale imaging data, such as neuroimaging studies.

Moreover, Quicksilver opens avenues for further research. Future work could explore enhancing prediction networks with more extensive multi-scale inputs or incorporating explicit domain-specific knowledge. Additionally, its framework suggests a path toward integrating predictive techniques with other advanced capabilities, such as joint image-label registration or tailored regularization models.

Conclusion

Quicksilver presents a sophisticated approach to bridging the computational gap in deformable image registration. By leveraging deep learning for predictive modeling, the method not only maintains the theoretical advantages of LDDMM but also achieves speed comparable to parametric models, making it suitable for large-scale and interactive analyses. This work paves the way for further exploration into integrating deep learning seamlessly into the domain of medical image registration.