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New AI model for cell segmentation and classification Deep learning technology outperforms traditional methods in biomedical image segmentation Researchers at Children’s Hospital of Philadelphia (CHOP) announced the creation of a new AI technology called CelloType, a comprehensive model designed to more accurately identify and classify cells in high-content tissue images. The findings were published in the journal Nature Methods. Spatial omics is a field of study that combines molecular profiling, such as genomics, transcriptomics or proteomics, with spatial information to map where different molecules are located within cells in complex tissues. It provides important, detailed insights into how a disease develops and progresses at the cellular level, aiding the advancement of precise diagnostics and targeted treatments, a major focus of CHOP’s translational research. This process allows researchers to study a broad range of complex diseases, such as cancer and chronic kidney disease, by revealing how cellular interactions and microenvironments contribute to disease progression and therapy response. As the critical first step of spatial omics data analysis, researchers undertake the tasks of cell segmentation (identifying cell boundaries) and classification (calling cell types).