Systems Biology Group Meeting

Presenter: Aidan Daly, Ph.D., Flatiron Research Fellow, Systems Biology Group

Topic: Common coordinate registration of high-resolution histology images.

Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. To tackle this problem, 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. I will discuss several potential approaches to automating this process with deep learning, most notably a novel convolutional neural network (CNN) architecture we have developed that combines (1) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface, and (2) a global segmentation CNN that operates on these extracted features. This hybrid network can be trained in an end-to-end manner, and we demonstrate its relative merits over competing approaches on a reference histology dataset as well as two published spatial transcriptomics datasets. We believe that this paradigm will greatly enhance our ability to process spatial -omics data, and has general purpose applications for the processing of high-resolution histology images on commercially available GPUs.