A convolutional neural network for common coordinate registration of high-resolution histology images.

ABSTRACT:

Motivation

Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform 'common coordinate registration', akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures.

Results

We present a novel convolutional neural network (CNN) architecture that combines (i) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface and (ii) a global segmentation CNN that operates on these extracte

SUBMITTER: Daly AC 

PROVIDER: S-EPMC9502165 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature