{"doi":"10.1101/2025.08.11.669599","title":"Anatomy-aware, label-informed approach improves image registration for challenging datasets","abstract":"Abstract Image registration-based volumetric morphometrics have emerged as a valuable method for identifying subtle morphological differences in neuroimaging and other biomedical images. However, accurate registration out-of-the-box remains challenging when overt morphological phenotypes—such as those observed in developmental and comparative studies—are present in a dataset. A new label-informed image registration function developed in the ANTsX ecosystem provides an easy to use, generalizable solution for anatomy-aware registration of a wide diversity of morphological variation. In this approach, segmentations ( i.e. , labels) provide a priori regional correspondences that guide the registration. These labels can be generated by any method–manually, using semi-automated tools, or through deep learning-based approaches–and allow morphological experts to define regions of correspondence based on biological concepts of homology ( e.g., tissue origin, gene expression patterns). Here we demonstrate the utility of this label-informed image registration approach for improving the registration knockout mouse embryos which fail to register to a wildtype (normative) template image by traditional registration methods. E15.5 Gli2 −/- mouse embryos show a severe scoliosis and radical topological rearrangement of the internal organs. Compared to traditional, intensity-only registration, the new label-informed image registration improved the correspondence of knockout subjects to the canonical template image, which resulted in increased power and sensitivity of downstream statistical analyses. All in all, label-informed image registration provides a flexible and customizable method to allow image registration in datasets for which registration-based morphometrics were previously unfeasible, unlocking new potential applications of registration-based morphometrics in developmental, comparative, and evolutionary studies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":558756,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9457,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":233665,"name":"Nicholas J. Tustison","orcid":"0000-0001-9418-5103","position":1,"is_corresponding":false},{"id":557936,"name":"A. Murat Maga","orcid":"0000-0002-7921-9018","position":2,"is_corresponding":false},{"id":782793,"name":"Rachel A. Roston","orcid":"0000-0002-1958-4849","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:55:30.312295Z","pmid":"40832189","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}