Benchmarking cell segmentation in two Spatialomics platforms with open-source software

The in situ tissue microenvironment is a complex mixture of different cell types, microanatomic structures, and acellular matrix material. Further, disease perturbs the morphologies of these features significantly. Spatial proteomics utilizing optical or mass spectrometer imaging simultaneously detects expression and subcellular localization of many proteins. Accurate identification of heterogeneous, irregular, and tightly interdigitating cell boundaries is a crucial step for all downstream analyses, including cell phenotyping and cell-cell interaction networks. We describe a benchmarking approach for rapid assessment of segmentation algorithm performance in melanoma and breast cancer contexts using mass spectrometer and optical spatial omics imaging platforms. Comparison of cell segmentation by fourteen methods to pathologist ground-truth annotation with Jaccard pixel index and F1 object measurement identified Cellpose models that performed well across different tumor samples and data sources with minimal fine-tuning. Algorithms utilizing cell membrane and nuclear features demonstrated better performance in both tumor samples. This benchmarking approach is a rapid and effective method to assess performance of cell segmentation methods in different contexts for efficient algorithm selection and iterative model training.