Affine Medical Image Registration with Coarse-to-Fine Vision Transformer (2203.15216v2)

Abstract: Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at https://github.com/cwmok/C2FViT.

• The paper introduces C2FViT as a novel method for 3D affine registration, addressing key limitations of CNN approaches.
• It combines global Vision Transformer capabilities with local convolutional operations to effectively capture both long-range dependencies and fine details.
• Experimental results demonstrate improved Dice scores, HD95 metrics, and broader generalizability under significant misalignment conditions.

Overview of "Affine Medical Image Registration with Coarse-to-Fine Vision Transformer"

The paper "Affine Medical Image Registration with Coarse-to-Fine Vision Transformer" by Mok and Chung presents a novel learning-based approach for 3D affine medical image registration. The authors critique existing convolutional neural network (CNN) methodologies for such tasks, citing their inadequacies in handling large spatial misalignments and their limited generalizability. In response, the authors propose the Coarse-to-Fine Vision Transformer (C2FViT) model to address these limitations through a structured exploitation of the Vision Transformer architecture and a multi-resolution strategy.

Main Contributions

1. Critique of CNN Approaches: The authors identify that existing CNN-based models focus predominantly on either local or global features, which makes them sensitive to the initial spatial alignment of inputs and often limits their applicability beyond the training dataset.
2. Vision Transformer for Affine Registration: C2FViT is introduced as a robust and efficient alternative. This approach leverages both the global connectivity inherent in Vision Transformers and local processing capabilities through convolutional operations. This hybridized approach ensures better registration performance by capturing both long-range dependencies and local features.
3. Coarse-to-Fine Strategy: C2FViT employs a coarse-to-fine strategy, progressively refining the alignment through staged processing of image resolutions. This strategy enhances registration accuracy by addressing large misalignments at lower resolutions and fine-tuning at higher resolutions.
4. Theoretical and Practical Evaluation: Evaluations performed on tasks such as 3D brain atlas registration and template-matching normalization demonstrate C2FViT's superiority over existing CNN-based methods, particularly in terms of robustness, accuracy, and generalizability across datasets.
5. Flexible Learning Paradigm: The proposed learning paradigm, incorporating both unsupervised and semi-supervised learning, allows for adaptation to varying registration contexts, potentially broadening the practical applicability of affine registration algorithms in clinical settings.

Experimental Results

The paper offers comprehensive experimental results indicating that C2FViT outperforms traditional CNN-based models in terms of Dice similarity coefficient, HD95, and register time. Notably, the model maintains strong performance even under conditions of significant initial misalignment, a scenario where many CNN-based approaches degrade. Importantly, C2FViT demonstrates considerable generalizability, achieving performance levels akin to conventional methods like ANTs, particularly when applied to unseen datasets.

Implications and Future Directions

The introduction of transformers to affine registration tasks marks a pivotal shift, indicating potential for similar methodologies in other image analysis domains. The reported generalizability suggests promising implications for clinical applications, where models are often transferred across varying data distributions.

Looking forward, expanding C2FViT training on diverse and larger datasets could further improve its robustness and accuracy. Additionally, the architectural flexibility of C2FViT allows potential extensions to incorporate domain-specific data augmentations or constraints, enhancing its adaptability to different medical imaging modalities. Future work could explore these enhancements and expand the application scope to multi-modal or even real-time registration scenarios.

In summation, this paper offers a compelling contribution to the field of medical image registration, leveraging the strengths of Vision Transformers to significantly advance the capabilities of affine registration methodologies. The insights gained have broad implications for future developments in AI-driven medical imaging applications.