If you’re delving into the fascinating world of medical image processing, VoxelMorph presents itself as a powerful ally. This library is tailored for learning-based tools for deformable image registration and alignment, making it an essential resource for both novices and experts alike. In this blog, we’ll walk you through getting started with VoxelMorph, providing a friendly and user-centric approach to this remarkable technology.

What is VoxelMorph?

To put it simply, VoxelMorph specializes in aligning images; think of it as a talented artist shaping two analog paintings, bringing them into harmony. It can learn from existing image datasets to understand how to align new images efficiently. VoxelMorph uses deep learning, a subset of artificial intelligence, to achieve this with remarkable accuracy.

How to Get Started with VoxelMorph

Here’s a streamlined guide to install and start using VoxelMorph

• Clone the repository from GitHub or simply use pip to install:

pip install voxelmorph

Exploring Tutorials

Before jumping into complex functionalities, it’s wise to familiarize yourself with the available tutorials:

• Main VoxelMorph Tutorial
• Deformable SynthMorph Demo
• Affine SynthMorph Demo
• CT-to-MRI SynthMorph Demo
• Training VoxelMorph Tutorial

Training Your Own Model

Imagine you’re a coach training an athlete (in this case, your model) to improve its performance. Here’s your game plan:

• Customize the data-loading code in voxelmorphgenerators.py to fit your dataset and format.
• Ensure your training data is in compatible formats like NIfTI, MGZ, or npz (numpy).
• Each npz file should have a vol parameter for image data and an optional seg variable for segmentation data.

To train the model, execute the following command:

python scripts/tftrain.py --img-list imageslist.txt --model-dir modelsoutput --gpu 0

Where imageslist.txt contains filenames of your training images.

Registration of Images

Once your model is set, registering images is like creating a captivating montage from a collection of photos. To do this, run the script:

python scripts/tfregister.py --moving moving.nii.gz --fixed atlas.nii.gz --moved warped.nii.gz --model model.h5 --gpu 0

This command creates a warped version of the moving image, accommodating alignments directly from your model.

Troubleshooting Tips

Things may not always go as planned. Here’s how you can tackle common roadblocks:

• If you encounter issues while installing, verify that all dependencies in setup.py are satisfied.
• Make sure that the formats of your training data are correct and consistent.
• While testing your model, ensure that your input images are properly normalized and in the correct affine space.

If problems persist, don’t hesitate to reach out for support. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.