Supplementary Figure 9: Examples of computational detection and segmentation of the nuclei in pre-expansion H&E images and post-expansion fluorescent images (ExPath).
From: Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy
Computational detection and segmentation of the nuclei is significantly more accurate in expanded samples as compared to pre-expanded samples: examples of normal breast, usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS). For the “expert annotation” and “automated segmentation” columns: green filled nuclei are nuclei segmented by the expert or the automated segmentation algorithm, respectively (red circles indicate nucleus outlines, which are not visible in the ExPath row because the resolution is too high and thus the outline is barely visible). In the “automated vs expert” column: green filled nuclei, true positives; red filled nuclei, false negatives; blue filled nuclei, false positives (note that when the automated segmentation yielded larger outlines than the expert, this is expressed as a blue “halo” around the green).
