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SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Istvan Grexa^1,2, Zsanett Zsófia Iván^1,3, Ede Migh¹

¹Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726.

Briefings in Bioinformatics

|March 14, 2024

Summary

We developed an unsupervised deep learning pipeline for multimodal image registration, crucial for analyzing biological samples. This method achieves accuracy comparable to supervised techniques without requiring expert annotations.

Keywords:

correlative microscopy deep learning microscopy unsupervised multimodal image registration

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Area of Science:

* Biomedical imaging
* Computational biology
* Machine learning for microscopy

Background:

* Correlative analysis of biological samples requires accurate registration of multimodal images.
* Current registration methods are often supervised, demanding limited expert-annotated data.
* Unsupervised approaches are needed to overcome data limitations in multimodal image registration.

Purpose of the Study:

* To propose a general unsupervised deep learning pipeline for multimodal image registration.
* To evaluate the pipeline's performance against state-of-the-art methods.
* To demonstrate the efficacy of unsupervised style transfer for registration.

Main Methods:

* Developed a deep learning pipeline for unsupervised multimodal image registration.
* Employed style transfer techniques to align different imaging modalities.
* Conducted comprehensive evaluations on four biological datasets using diverse microscopy techniques.

Main Results:

* The proposed unsupervised pipeline achieved image registration accuracy comparable to supervised methods.
* Style transfer combined with unsupervised training proved effective for multimodal registration.
* The method successfully registered images from various microscopy modalities without human intervention.

Conclusions:

* Unsupervised deep learning, particularly with style transfer, offers a viable solution for multimodal image registration.
* This approach overcomes the limitations of expert-annotated data in supervised methods.
* The pipeline provides a generalizable and efficient tool for correlative biological imaging analysis.