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

Appendix 4—figure 1. Precision-recall curves on individual stacks for different CNN variants on the ovule (A) and lateral root primordia (B) datasets.

Appendix 4—figure 1.

Efficiency of boundary prediction was assessed for seven training procedures that sample different type of architecture (3D U-Net vs. 3D Residual U-Net), loss function (BCE vs. Dice vs. BCE-Dice)) and normalization (Group-Norm (GN) vs. Batch-Norm (BN)). The larger the area under the curve, the better the precision. Source files used to generate the precision-recall curves are available in the Appendix 4—figure 1—source data 1.

Appendix 4—figure 1—source data 1. Source data for precision/recall curves of different CNN variants evaluated on individual stacks.
'pmaps_root' contains precision/recall values computed on the test set from the Lateral Root dataset, 'pmaps_ovules' contains precision/recall values computed on the test set from the Ovules dataset, 'fig2_precision_recall.ipynb' is a Jupyter notebook generating the plots.