I am a developer at Johns Hopkins University's Neurodata Lab and am writing a pure-python package called ARDENT that does fairly sophisticated 3D image registration. The core algorithm is based on this paper that our group produced in 2005, which has over 1200 citations and has become a standard in the field of medical image registration: Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms, available at https://link.springer.com/article/10.1023/B:VISI.0000043755.93987.aa ARDENT works across multiple modalities (images generated by different methods, e.g. fMRI, light microscopy, etc.), and although it has convenient presets written with neuroscience applications in mind, it is fully general to all manner of image registration tasks. I would ideally like to start a conversation but I have a few specific questions. -- Do you think Scikit-Image is the place for a program like the one I have described? -- Would it make more sense for our package to be incorporated into an existing Scikit-Image framework like the Transform module or included as an insular, standalone component? It seems like there is some functional overlap with Transform.estimate_transform so I suspect this might be the most logical place for ARDENT. -- Would Scikit-Image would be comfortable with PyTorch as a dependency or as an optional dependency? The core LDDMM implementation that does ARDENT's heavy lifting utilizes PyTorch for GPU acceleration, but it could feasibly be rewritten without PyTorch. Thank you very much, Devin Crowley JHU Research Assistant