Spatial landmark detection and tissue registration with deep learning

Abstract

AbstractSpatial 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 non-linear 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 a new landmark detection and registration method, utilizing neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets, demonstrating superior performance in both accuracy and stability compared to existing approaches.