Extended Data Figure 2.

a, PanopticNet architecture. Images are fed into a ResNet50 backbone coupled to a feature pyramid network. Two semantic heads produce pixel-level predictions. The first head predicts whether each pixel belongs to the interior, border, or background of a cell, while the second head predicts the center of each cell. b, Relative proportion of preprocessing, inference, and post-processing time in PanopticNet architecture. c, Evaluation of precision, recall, and Jaccard index for Mesmer and previously published models (right) and models trained on TissueNet (left). d, Summary of TissueNet accuracy for Mesmer and selected models to facilitate future benchmarking efforts e,f Breakdown of most prevalent error types (e) and less prevalent error types (f) for Mesmer and previously published models illustrates Mesmer’s advantages over previous approaches. g, Comparison of the size distribution of prediction errors for Mesmer (left) with nuclear segmentation followed by expansion (right) shows that Mesmer’s predictions are unbiased.