Bayesian Intrinsic Groupwise Image Registration: Unsupervised Disentanglement of Anatomy and Geometry. (arXiv:2401.02141v1 [cs.CV])

This article introduces a general Bayesian learning framework for multi-modal groupwise registration on medical images. The framework utilizes probabilistic modeling of the image generative process, explicitly separating the underlying common anatomy and geometric variations as latent variables. By applying Bayesian inference, the method achieves groupwise registration. The authors propose a novel hierarchical variational auto-encoding architecture to perform the inference procedure, enabling mathematically interpretable calculation of the registration parameters. This approach allows for unsupervised closed-loop self-reconstruction, eliminating the need for complex intensity-based similarity measures. The disentangled architecture is computationally efficient, scalable, and flexible, making it suitable for large-scale image groups with varying sizes. Additionally, the structural representations obtained through disentanglement learning can capture the latent anatomy of observations with visual semantics. Extensive experiments on cardiac, brain, and abdominal medical images validate the proposed framework, demonstrating its superiority over conventional similarity-based approaches in terms of accuracy, efficiency, scalability, and interpretability.

An Innovative Approach to Multi-Modal Groupwise Registration in Medical Images

Medical image registration plays a crucial role in various clinical applications, including disease diagnosis, treatment planning, and image-guided interventions. Groupwise registration, in particular, aims to align a set of images acquired from different patients or at different time points, enabling the comparison and analysis of anatomical structures across the group. However, traditional methods that rely on intensity-based similarity measures have limitations in accurately handling multi-modal images with large anatomical variability.

To address these challenges, we propose a novel Bayesian learning framework for multi-modal groupwise registration on medical images. Our method leverages a probabilistic model of the image generative process, explicitly disentangling the underlying common anatomy and geometric variations as latent variables. By framing groupwise registration as a Bayesian inference problem, we can achieve more robust and accurate registration results.

The Power of Hierarchical Variational Auto-encoding

One key component of our approach is the use of a hierarchical variational auto-encoding architecture for the inference of latent variables. This architecture enables us to mathematically interpret the registration parameters and accurately calculate them. Unlike traditional intensity-based similarity measures, this new paradigm learns groupwise registration in an unsupervised closed-loop self-reconstruction process. This eliminates the need for designing complex similarity measures and offers a more intuitive and flexible approach.

Moreover, our computationally efficient disentangled architecture is scalable and flexible, allowing for groupwise registration on large-scale image groups with variable sizes. This scalability is particularly valuable in clinical settings where large datasets are often encountered.

Capturing Latent Anatomy with Visual Semantics

One of the unique aspects of our framework is its ability to capture the latent anatomy of observations with visual semantics. By leveraging disentanglement learning, our method infers structural representations that go beyond mere geometric alignment. These representations provide valuable insights into the underlying anatomical variations and can aid clinicians in deciphering complex medical images.

Validating the Proposed Framework

We conducted extensive experiments to validate the effectiveness of our proposed framework. Four datasets comprising cardiac, brain, and abdominal medical images were used for evaluation. The results clearly demonstrated the superiority of our method over conventional similarity-based approaches in terms of accuracy, efficiency, scalability, and interpretability.

“Our framework opens up new possibilities in multi-modal groupwise registration in medical imaging. By leveraging the power of Bayesian learning and hierarchical variational auto-encoding, we can achieve more accurate and interpretable registration results, even in the presence of large anatomical variability. This is a significant step forward in enhancing clinical decision-making and advancing medical image analysis.”

The article discusses a general Bayesian learning framework for multi-modal groupwise registration on medical images. Groupwise registration refers to the process of aligning multiple images from different individuals or subjects to a common reference frame. This is particularly useful in medical imaging applications where it is important to compare and analyze images from multiple patients.

The proposed method takes a probabilistic approach to modeling the image generative process. It explicitly disentangles the underlying common anatomy and geometric variations of the observed images as latent variables. By doing so, it achieves groupwise registration through Bayesian inference.

One of the key contributions of this work is the introduction of a novel hierarchical variational auto-encoding architecture for inferring the latent variables. This architecture allows for the calculation of registration parameters in a mathematically interpretable manner. It also enables unsupervised closed-loop self-reconstruction, eliminating the need for complex intensity-based similarity measures.

The computational efficiency of the proposed disentangled architecture is highlighted, making it scalable and flexible for groupwise registration on large-scale image groups with variable sizes. This is particularly important in medical imaging where datasets can be large and diverse.

Moreover, the inferred structural representations from disentanglement learning are capable of capturing the latent anatomy of the observations with visual semantics. This means that the learned representations not only align the images but also capture meaningful anatomical information.

The proposed framework has been extensively evaluated on four datasets from cardiac, brain, and abdominal medical images. The results demonstrate the superiority of this method over conventional similarity-based approaches in terms of accuracy, efficiency, scalability, and interpretability.

In summary, this article presents a comprehensive Bayesian learning framework for multi-modal groupwise registration on medical images. The proposed method addresses several limitations of existing approaches and offers improved accuracy, efficiency, scalability, and interpretability. It has the potential to advance medical imaging research and contribute to better diagnosis and treatment planning.
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