Spatial landmark detection and tissue registration with deep learning.

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 rang

SUBMITTER: Ekvall M 

PROVIDER: S-EPMC11009106 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature