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- W4387346516 abstract "Next-generation sequencing (NGS) technologies made it possible to study the cell structure and composition of a given tissue while preserving spatial context, giving birth to the field of spatial transcriptomics. However, NGS technologies such as Visium provide gene counts at spot locations that contain up to 50 cells which limits researchers from studying the tissue at a single-cell level. Computational techniques such as Cell2Location can deconvolve the gene counts to obtain the proportions of cell types at each spot. Those approaches, however, do not provide a way to assign the predicted cell types to the individual cells within a Visium spot. In parallel, the digital pathology field has seen rapid growth with the introduction of deep convolutional neural networks that can segment and classify cells with high accuracies, matching the level of the pathologist." @default.
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- W4387346516 date "2023-09-03" @default.
- W4387346516 modified "2023-10-06" @default.
- W4387346516 title "Deep-Learning Based Cell Segmentation and Deconvolution in Spatial Transcriptomics" @default.
- W4387346516 doi "https://doi.org/10.1145/3584371.3613050" @default.
- W4387346516 hasPublicationYear "2023" @default.
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