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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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Encoding matching criteria for cross-domain deformable image registration.

Zhuoyuan Wang¹, Haiqiao Wang¹, Dong Ni¹

¹Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.

Medical Physics

|December 17, 2024

Summary

This study introduces a novel deep learning approach for cross-domain deformable medical image registration, improving accuracy and adaptability. The method excels in both single-domain and challenging cross-domain tasks, outperforming existing techniques.

Keywords:

cross‐domain registration deformable image registration domain adaptation matching criterion one‐shot learning

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Area of Science:

Medical Imaging
Deep Learning
Computer Vision

Background:

Existing deep learning registration models struggle with cross-domain tasks due to single-domain training.
Retraining models for new scenarios is time-consuming and data-intensive.
There is a need for efficient and accurate cross-domain deformable registration solutions.

Purpose of the Study:

To develop a registration-oriented encoder that models matching criteria for enhanced accuracy and adaptability.
To enable efficient adaptation to different medical image domains.

Main Methods:

A general feature encoder (Encoder-G) captures comprehensive image features.
A structural feature encoder (Encoder-S) encodes structural self-similarity.
One-shot learning updates Encoder-S for domain adaptation, evaluated on MRI brain, abdomen, and cardiac images.

Main Results:

Achieved superior performance in single-domain tasks, outperforming comparison methods.
Outperformed other deep networks in most cross-domain scenarios without one-shot optimization.
Successfully surpassed traditional methods (SyN) in all cross-domain scenarios after one-shot optimization.

Conclusions:

The proposed method demonstrates favorable single-domain performance.
The approach shows improved generalization and adaptation for cross-domain registration.
The method is feasible for challenging cross-domain registration applications.