JSON
Ekvall M, Bergenstråhle L, Andersson A, Czarnewski P, Olegård J, Käll L, Lundeberg J
Nat. Methods 21 (4) 673-679 [2024-04-00; online 2024-03-04]
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.
PubMed 38438615
DOI 10.1038/s41592-024-02199-5
Crossref 10.1038/s41592-024-02199-5
pmc: PMC11009106
pii: 10.1038/s41592-024-02199-5