Figure 3: Mesmer performs whole-cell segmentation across tissue types and imaging platforms with human-level accuracy.

a, Sample images, predicted segmentations, and F1 scores for distinct tissues and imaging platforms visually demonstrate that Mesmer delivers accurate cell segmentation for all available imaging platforms. b, Mesmer has accuracy equivalent to specialist models trained only on data from a specific imaging platform (Methods), with all models evaluated on data from the platform used for training. c, Mesmer has accuracy equivalent to specialist models trained only on data from a specific tissue type (Methods), with all models evaluated on data from the tissue type used for training. GI, gastrointestinal. d, F1 scores evaluating the agreement between segmentation predictions for the same set of images. The predictions from five independent expert annotators were compared against each other (human vs. human) or against Mesmer (human vs. Mesmer). No statistically significant differences between these two comparisons were found, demonstrating that Mesmer achieves human-level performance. e, Workflow for pathologists to rate the segmentation accuracy of Mesmer compared with expert human annotators. f, Pathologist scores from the blinded comparison. A positive score indicates a preference for Mesmer while a negative score indicates a preference for human annotations. Pathologists displayed no significant preference for human labels or Mesmer’s outputs overall. When broken down by tissue type, pathologists displayed a slight preference for Mesmer in immune tissue (p=0.02), and a slight preference for humans in colon tissue (p=0.01), again demonstrating that Mesmer has achieved human-level performance. n.s., not significant; *p<0.05, two-sample t-test for d, one-sample t-test for f. All scale bars are 50 μm.