ArticleNature methods2024
Spatial landmark detection and tissue registration with deep learning.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 30 citations in OpenAlex.
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- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- An in vivo platform to jointly monitor cellular and metabolic responses to chemotherapy.bioRxiv : the preprint server for biology · 2026Article
- SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.Briefings in bioinformatics · 2026Article
- Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel.PLoS computational biology · 2026Article
- Automated deep-learning quantification of nine patellofemoral instability parameters on multislice CT images : development and validation of the GU2Net model.Bone & joint open · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- An integrative spatial multi-omic workflow for unified analysis of tumor tissue.Cell reports methods · 2026Article
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- On the degrees of freedom of gridded control points in learning-based medical image registration.Medical physics · 2026Article
- High-parameter spatial multi-omics through histology-anchored integration.Nature methods · 2026Article
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies.Nature communications · 2026Article
- Artificial intelligence-assisted remote sensing observation, understanding, and decision.Innovation (Cambridge (Mass.)) · 2025Review
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Combining spatial transcriptomics with tissue morphology.Nature communications · 2025Review
- Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities.Small methods · 2025Review
- Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution.ACS nano · 2025Review
- AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.Neuroinformatics · 2024Article
- Spatial landscapes of cancers: insights and opportunities.Nature reviews. Clinical oncology · 2024Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.