Spatial landmark detection and tissue registration with deep learning.
Nat Methods
; 21(4): 673-679, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-38438615
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article