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Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

With increasing demand to correlate data across multiple imaging modalities, Race and colleagues demonstrate a mechanism by which annotations can be generated on images from one imaging modality and transferred to a second image modality for data integration (and optionally, back again). Further, they perform this workflow on mass spectrometry images of a pancreatic cancer mouse model and hematoxylin and eosin-stained sections. HALO and HALO AI image analysis software was used to develop a DenseNet algorithm to classify and generate annotations for pancreatic ductal carcinoma tumor, non-neoplastic acinar tissue, and connective tissue on H&E-stained slides. This method for bidirectional transfer of image annotations may enable novel workflows in the future.

Race AM, Sutton D, Hamm G, Maglennon G, Morton JP, Strittmatter N, Campbell A, Sansom OJ, Wang Y, Barry ST, Takáts Z, Goodwin RJA, Bunch J

Analytical Chemistry | First published 03 February 2021| DOI https://doi.org/10.1021/acs.analchem.0c02726