{"entity": "publication", "iuid": "2a8bf0b967664c4fa51e9244de67c3dc", "timestamp": "2026-08-20T20:48:43.528Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/2a8bf0b967664c4fa51e9244de67c3dc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/2a8bf0b967664c4fa51e9244de67c3dc"}}, "title": "Spatial landmark detection and tissue registration with deep learning.", "authors": [{"family": "Ekvall", "given": "Markus", "initials": "M", "orcid": "0000-0001-6942-0458", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/18a639299c9b4970ab7be3d5f08723e9.json"}}, {"family": "Bergenstr\u00e5hle", "given": "Ludvig", "initials": "L", "orcid": "0000-0002-5108-4481", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/60ea7a079ae04b8896196dafec84a6fc.json"}}, {"family": "Andersson", "given": "Alma", "initials": "A", "orcid": "0000-0002-4773-9975", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82aebb401f644b2fbd015827dc916078.json"}}, {"family": "Czarnewski", "given": "Paulo", "initials": "P"}, {"family": "Oleg\u00e5rd", "given": "Johannes", "initials": "J", "orcid": "0000-0001-9082-4318", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bc00fb7e52b44bb3b6dc9a88fc3cfeac.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Lundeberg", "given": "Joakim", "initials": "J", "orcid": "0000-0003-4313-1601", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d9fa47767cd14ef2b9528c8b998cf095.json"}}], "type": "journal article", "published": "2024-04-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "21", "issue": "4", "pages": "673-679", "issn-l": "1548-7091"}, "abstract": "Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle nonlinear deformations between tissue sections and are ineffective for z-stack alignment, other modalities beyond image data or multimodal data. We address these challenges by introducing effortless landmark detection, a new unsupervised landmark detection and registration method using neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets including histology and spatially resolved transcriptomics, demonstrating superior performance in both accuracy and stability compared to existing approaches.", "doi": "10.1038/s41592-024-02199-5", "pmid": "38438615", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11009106"}, {"db": "pii", "key": "10.1038/s41592-024-02199-5"}], "notes": [], "created": "2026-08-20T09:03:01.754Z", "modified": "2026-08-20T09:03:01.917Z"}