Thank you so much for having been part of the journey 🙏

Thank you!

Finally, I want to give a massive thanks to the amazing team behind CLEM-Reg 🙏

@matous-elphick.bsky.social, @lainebioimaging.bsky.social @henriqueslab.bsky.social, @em-stp.bsky.social, @martinjones.bsky.social, MC, Chris and Cameron

The plugin can also be run in separate steps for debugging and to use different segmentation algorithms for registration. (9/9)

www.youtube.com/watch?v=cypD...

💻 You can directly try out CLEM-Reg for yourself with our @napari.org plugin where we also include a sample benchmark dataset.

Installation instructions are here 👉 github.com/krentzd/napa... (8/9)

www.youtube.com/watch?v=ud3z...

📊🎯 We evaluated the performance of CLEM-Reg against an expert electron microscopist using lysosomes as target structures and found that CLEM-Reg aligns lysosomes with submicron accuracy (7/9)

3️⃣ Finallly, the fluorescence image is overlaid to the electron microscopy image with the transform found from the point cloud registration. (6/9)

2️⃣ Then, we sample point clouds from the segmentations to obtain a modality-agnostic and lightweight representation. The two resulting point clouds are then registered using a probabilistic point cloud registration algorithm. (5/9)

1️⃣ CLEM-Reg starts off by segmenting mitochondria in both the fluorescence and the electron microscopy images. Mitochondria work well, because they're usually very well distributed across the cell. (4/9)

And this is why we developed CLEM-Reg, an algorithm that automatically and accurately registers vCLEM images in minutes. (3/9)

For most image registration tasks, intensity-based methods work well. Correlative light and electron microscopy data are, however, a lot trickier to align, because of the massive differences in visual appearance. As a result, CLEM data is usually registered manually. (2/9)