Performance of Image Registration Tools on High-Resolution 3D Brain Images
This work addresses the problem of image registration for researchers analyzing high-resolution 3D brain images, highlighting performance gaps in existing tools.
The study evaluated five image registration tools on high-resolution 3D brain images from mouse brains cleared with the CUBIC protocol, finding that ANTS provided the best accuracy and Elastix had the highest computational efficiency with acceptable accuracy.
Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution. These methods produce very large 3D images (several gigabytes for a whole mouse brain). A necessary step in analysing these images is registration across samples. Existing methods of registration were developed for lower resolution image modalities (e.g. MRI) and it is unclear whether their performance and accuracy is satisfactory at this larger scale. In this study, we used data from different mouse brains cleared with the CUBIC protocol to evaluate five freely available image registration tools. We used several performance metrics to assess accuracy, and completion time as a measure of efficiency. The results of this evaluation suggest that the ANTS registration tool provides the best registration accuracy while Elastix has the highest computational efficiency among the methods with an acceptable accuracy. The results also highlight the need to develop new registration methods optimised for these high-resolution 3D images.