{"doi":"10.3390/app11041892","title":"Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration","abstract":"Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.","journal":"Applied Sciences","year":2021,"id":169849,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":28,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9518,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":103904,"name":"Sarthak Pati","orcid":"0000-0003-2243-8487","position":1,"is_corresponding":false},{"id":70257,"name":"Michael D. Feldman","orcid":"0000-0002-6661-4940","position":2,"is_corresponding":false},{"id":74579,"name":"MacLean P. Nasrallah","orcid":"0000-0003-4861-0898","position":3,"is_corresponding":false},{"id":259082,"name":"Paul A. Yushkevich","orcid":"0000-0001-8543-4016","position":4,"is_corresponding":false},{"id":103874,"name":"Spyridon Bakas","orcid":"0000-0001-8734-6482","position":5,"is_corresponding":false},{"id":259075,"name":"Ludovic Venet","orcid":"0000-0003-4203-5378","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-18T23:46:15.560174Z","pmid":"34290888","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":[]}