This repository contains the code for preprocessing, co-registering , and assessing the co-registration performance of methods applied to mesoscopic photoacoustic imaging (PAI) data, presented in the study by Lefebvre TL et al. (Lefebvre TL et al. Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature. Phys Med Biol. 2024.). Three categories of co-registration techniques are tested:
- Intensity-based co-registration (using joint histogram mutual information or normalized cross-correlation as an optimization metric for affine image co-registration)
- Shape-based co-registration (using the point-to-plane iterative closest point algorithm or normalized cross-correlation on distance-transformed segmentations for affine image co-registration)
- Deep learning-based segmentation (using dual-input LocalNet for learned deformable image co-registration)
This work is largerly based on contributions and Python packages developed by others and reported previously, mainly:
SimpleITK(Lowekamp et al. The Design of SimpleITK. Frontiers in Neuroinformatics. 2013 Dec 30;7:45)Open3D(Zhou et al. Open3D: A Modern Library for 3D Data Processing. arXiv. 2018. arXiv:1801.09847)MONAI(Cardoso et al. MONAI: An open-source framework for deep learning in healthcare. arXiv. 2022. arXiv:2211.02701)
