High-fidelity multiclass instance segmentation of cells for spatial proteomics
Abstract
High quality cell segmentations across cell classes remain elusive in spatial proteomics. Existing methods rely on expansion of cell nuclei, or on “pan-membrane” markers that are not actually uniformly expressed across all cells and tissues. Here, we present an extension of pseudo-spectral angle mapping (pSAM) for multiclass instance segmentation of cells. By predicting segmentations on class maps rather than images, cell shape is better captured. Improved representation of cell shape could help better infer interaction of cells in static images.
Fields of Study & Subjects
Cell Image Analysis Techniques
Single-cell and spatial transcriptomics
Medical Image Segmentation Techniques
Publication details
- Journal / source
- Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues
- Pages
- 15–15
- Publication date
- 4 March 2026
- Language
- EN