How I Learned to Align Brain MRIs with VoxelMorph (and Didn’t Lose My Mind in the Process)

Medical imaging might not sound like the most thrilling topic, but stay with me. Imagine you’re working with 3D brain MRIs, trying to analyze scans that look like someone took a puzzle, shook it really hard, and threw it on the table. One scan’s left hemisphere is chilling where the frontal lobe should be, while the other scan is just... confusing.

This is where image registration comes in, aligning scans so they match up perfectly. It’s the unsung hero of medical imaging, the thing that makes everything else work. Without it, comparing scans is like trying to organize socks without a laundry basket.

Traditional registration methods like ANTs and elastix are great but slow like molasses-in-winter slow. And they require so much manual tuning that you might as well start calling yourself a registration wizard.

That’s when I decided to try VoxelMorph, a deep learning tool that promises speed, accuracy, and a lot less headache. The idea was simple: train two models with different loss functions and figure out which one works best for aligning brain MRIs. Spoiler: It wasn’t as simple as I thought, but it was worth it.

What Is Registration, and Why Should You Care?

Let’s start with the basics. Registration is about aligning images so that every part of one scan matches the same part in another. In medical imaging, this is crucial for comparing patient scans over time, planning surgeries, or even doing research.

But here’s the problem: brains don’t cooperate. Scans from different patients or even the same patient at different times can look wildly different. It’s like trying to stack Jenga blocks when someone keeps moving the table.

My Mission: SSD vs. NCC

To test VoxelMorph, I trained two models using two different loss functions:

1. SSD (Sum of Squared Differences): This measures pixel by pixel intensity differences between the fixed and warped images. Think of it as a perfectionist, but the kind that freaks out if the lighting isn’t perfect.

2. NCC (Normalized Cross-Correlation): This looks at patterns in local intensities instead of individual pixels. It’s like that chill friend who says, “Eh, close enough,” but still manages to ace the exam.

Both models also used a gradient loss to keep the deformation fields smooth. Nobody wants registration that looks like a brain just came back from a theme park rollercoaster.

The Dataset: All the Brains

My dataset had 48 training MRIs and 11 test MRIs, each annotated with 35 brain regions. This gave me:

- 2,256 training pairs (because every MRI had to align with every other MRI, like speed dating for scans),

- 110 test pairs for evaluation.

To make life easier (for the models, not me), I resized all images to 128 x 128 x 128. Segmentations (the labels for brain regions) were resized with extreme care using nearest-neighbor interpolation. If you’re wondering why this step matters, think of it like making pancakes you don’t want to overmix the batter or everything gets mushy.

Training the Models: A Crash Course in Patience

Training deep learning models sounds intimidating, but once you set it up, it’s mostly waiting. For SSD and NCC, I used:

- The Adam optimizer (basically the espresso of optimizers: it gets the job done fast),

- A learning rate of 0.0001(with a scheduler),

- five epochs of training (because my GPU was already sweating).

five epochs might sound short, but it was enough to get the models to learn the basics. That said, if I could go back, I’d train for longer because who doesn’t want more polished results?

Registration in Action

Testing the models on five random test pairs was where the magic or chaos happened. Here’s what the process looked like:

1. Moving Image: The MRI I wanted to align.

2. Fixed Image: The target MRI.

3. Warped Image: The result after applying the deformation field.

Before registration, the scans didn’t match up at all. Imagine trying to put together IKEA furniture without instructions, that’s how misaligned they were.

After registration:

- SSD did okay, but it struggled when the images had different brightness levels. It’s like trying to compare apples to oranges when one of them is glowing.

- NCC, on the other hand, was a rockstar. The alignments were smooth, accurate, and made me feel like a registration genius (for about five minutes).

The deformation fields were something else entirely. Watching the moving image stretch and warp to align with the fixed image was oddly satisfying, like watching slime videos on Instagram. ??

Crunching the Numbers

To measure how well the models performed, I used two metrics:

1. Dice Coefficient: Think of this as overlap percentage. Higher is better, with 1.0 being perfect. It’s like a puzzle you want the pieces to fit perfectly.

2. Hausdorff Distance: This measures the biggest mismatch between boundaries. Lower is better. It’s basically the “how badly did we miss the mark?” metric.

NCC took things to the next level. It handled brightness variations gracefully, aligning regions more accurately and producing smoother deformation fields. Imagine fixing not just the picture frame but also the wall, it just worked.

What I Learned

1. NCC for the Win: If your data has brightness variations, NCC is your go-to. SSD is fine, but it’s picky.

2. Visualization is Gold: Seeing the warped images and deformation fields made it easy to spot problems. Plus, it’s oddly fun to watch brains stretch like taffy.

3. Preprocessing is King: Skipping proper resizing would have been like skipping leg day at the gym. It might not seem important, but you’ll regret it later.

4. Short Training Can Work: Three epochs got me solid results, though I’m sure a longer run would’ve been even better.

What’s Next?

This project was just the beginning. Here’s where I’d like to take it next:

- Data Augmentation: Adding rotations, flips, and intensity shifts to make the models more robust.

- Hybrid Loss Functions: Combining SSD’s precision with NCC’s flexibility.

- Real-World Testing: Applying the models to clinical datasets and seeing how they hold up in the wild.

Final Thoughts

VoxelMorph turned something that felt overwhelming into something manageable and even fun. Sure, there were moments I wanted to throw my laptop out the window (looking at you, segmentation resizing), but the results made it worth it??.

If you’re thinking about tackling a similar project, my advice is simple: start small, trust the process, and don’t be afraid to make mistakes. And when things go sideways and they will just remember that even failed experiments teach you something.

Got questions, comments, or your own registration horror stories? connect and DM. Let’s chat. Until then, here’s to better aligned brains and fewer headaches!