{"url":"/task/diffeomorphic-medical-image-registration","name":"Diffeomorphic Medical Image Registration","slug":"diffeomorphic-medical-image-registration","description_markdown":"Diffeomorphic mapping is the underlying technology for mapping and analyzing information measured in human anatomical coordinate systems which have been measured via Medical imaging. Diffeomorphic mapping is a broad term that actually refers to a number of different algorithms, processes, and methods. It is attached to many operations and has many applications for analysis and visualization. Diffeomorphic mapping can be used to relate various sources of information which are indexed as a function of spatial position as the key index variable. Diffeomorphisms are by their Latin root structure preserving transformations, which are in turn differentiable and therefore smooth, allowing for the calculation of metric based quantities such as arc length and surface areas. Spatial location and extents in human anatomical coordinate systems can be recorded via a variety of Medical imaging modalities, generally termed multi-modal medical imagery, providing either scalar and or vector quantities at each spatial location.\r\n\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Quicksilver](https://arxiv.org/pdf/1703.10908.pdf) )</span>","categories":[{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":11,"papers_with_code":7,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/diffeomorphic-medical-image-registration-on","slug":"diffeomorphic-medical-image-registration-on","dataset":"OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP","dataset_url":"/dataset/ppmi","rows_in_archive":6,"metrics":["Dice (Average)","CPU (sec)","GPU sec","Neg Jacob Det","Dice (SE)","Dice"],"first_row_in_archive_order":{"model":"NiftyReg (CC)","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/diffeomorphic-medical-image-registration-on-1","slug":"diffeomorphic-medical-image-registration-on-1","dataset":"Automatic Cardiac Diagnosis Challenge (ACDC)","dataset_url":"/dataset/acdc","rows_in_archive":3,"metrics":["Dice","RMSE","Hausdorff Distance (mm)","Grad Det-Jac"],"first_row_in_archive_order":{"model":"cVAE Diffeomorphic (S3)","paper_title":"Learning a Probabilistic Model for Diffeomorphic Registration","paper_url":"/paper/learning-a-probabilistic-model-for","paper_date":"2018-12-18","arxiv_id":"1812.07460","code_links":[],"syntology":null}},{"leaderboard":"/sota/diffeomorphic-medical-image-registration-on-2","slug":"diffeomorphic-medical-image-registration-on-2","dataset":"CUMC12","dataset_url":null,"rows_in_archive":3,"metrics":["Mean target overlap ratio"],"first_row_in_archive_order":{"model":"Metric Net (Local Reg)","paper_title":"Metric Learning for Image Registration","paper_url":"/paper/metric-learning-for-image-registration","paper_date":"2019-04-21","arxiv_id":"1904.09524","code_links":[{"title":"uncbiag/registration","url":"https://github.com/uncbiag/registration"}],"syntology":null}}],"datasets":[{"url":"/dataset/ppmi","name":"PPMI","full_name":"Parkinson’s Progression Markers Initiative","num_papers_in_archive":87},{"url":"/dataset/acdc","name":"ACDC","full_name":"Automated Cardiac Diagnosis Challenge","num_papers_in_archive":52}],"subtasks":[],"parent_tasks":[{"url":"/task/medical-image-registration","name":"Medical Image Registration"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); 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