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. 2020 Jul 29;9:e57613. doi: 10.7554/eLife.57613

Figure 3. Segmentation using graph partitioning.

Figure 3.

(A–C) Quantification of segmentation produced by Multicut, GASP, Mutex watershed (Mutex) and DT watershed (DT WS) partitioning strategies. The Adapted Rand error (A) assesses the overall segmentation quality whereas VOImerge (B) and VOIsplit (C) assess erroneous merge and splitting events (lower is better). Box plots represent the distribution of values for seven (ovule, magenta) and four (LRP, green) samples. (D, E) Examples of segmentation obtained with PlantSeg on the lateral root (D) and ovule (E) datasets. Green boxes highlight cases where PlantSeg resolves difficult cases whereas red ones highlight errors. We obtained the boundary predictions using the generic-confocal-3d-unet for the ovules dataset and the generic-lightsheet-3d-unet for the root. All agglomerations have been performed with default parameters. 3D superpixels instead of 2D superpixels were used. Source files used to create quantitative results shown in (A–C) are available in the Figure 3—source data 1.

Figure 3—source data 1. Source data for panes A, B and C in Figure 3.
The archive contains CSV files with evaluation metrics computed on the Lateral Root and Ovules test sets. 'root_final_16_03_20_110904.csv' - evaluation metrics for the Lateral Root, 'ovules_final_16_03_20_113546.csv' - evaluation metrics for the Ovules, 'fig3_evaluation_and_supptables.ipynb' - Juputer notebook for generating panes A, B, C in Figure 3 as well as Appendix 5—table 2.