{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:51:17Z","timestamp":1778601077650,"version":"3.51.4"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform \u2018common coordinate registration\u2019, 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All code is publicly available at https:\/\/github.com\/flatironinstitute\/st_gridnet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab447","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T15:12:33Z","timestamp":1623683553000},"page":"4216-4226","source":"Crossref","is-referenced-by-count":18,"title":["A convolutional neural network for common coordinate registration of high-resolution histology images"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-8623","authenticated-orcid":false,"given":"Aidan C","family":"Daly","sequence":"first","affiliation":[{"name":"Center for Computational Biology, Flatiron Institute , New York, NY 10010, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krzysztof J","family":"Geras","sequence":"additional","affiliation":[{"name":"Center for Data Science, New York University , New York, NY 10011, USA"},{"name":"Department of Radiology, Grossman School of Medicine, New York University , New York, NY 10016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Bonneau","sequence":"additional","affiliation":[{"name":"Center for Computational Biology, Flatiron Institute , New York, NY 10010, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"2023051607120780600_btab447-B1","doi-asserted-by":"crossref","first-page":"1900221","DOI":"10.1002\/bies.201900221","article-title":"Spatially resolved transcriptomes\u2014next generation tools for tissue exploration","volume":"42","author":"Asp","year":"2020","journal-title":"BioEssays"},{"key":"2023051607120780600_btab447-B2","author":"Deng","year":"2009"},{"key":"2023051607120780600_btab447-B3","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1038\/nmeth.4634","article-title":"Identification of spatial expression trends in single-cell gene expression data","volume":"15","author":"Edsgard","year":"2018","journal-title":"Nat. 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