3d_nuclei_seg_benchmark
Benchmarking of segmentation methods for 3D images of plant nuclei. Images are segmented. The segmentation is then evaluated with metrics (see 'metrics' section for more details).
dataset
3D images of plant nuclei. An image contains a single nucleus. The ground truth segmentation was partially done thanks to NucleusJ and manually.
metrics
- 3D jaccard index.
- 3D dice index.
- Nucleusj morphology metrics.
setups
Each folder has to be setup with a Docker specific environment.
todo
2d (applied slice by slice)
- [2d_maskrcnn] Mask-RCNN: https://github.com/matterport/Mask_RCNN
- [2d_topcoders] DSB2018 winner team (3 codes available: selim, albu and victor): https://github.com/selimsef/dsb2018_topcoders
- [2d_stardist] Stardist: https://github.com/stardist/stardist
3d
- [3d_stardist] Stardist (no pretrained models...): https://github.com/stardist/stardist
work in progress
2d
- [2d_maskrcnn] trying...
- [2d_topcoders] DSB2018 winner team: selim's code outputs only black images (still work in progress)
results
For now the results are computed on the OMERO_FSU dataset with the Otsu segmentation considered as the ground truth. This will be change later.
Method | 2D or 3D? | Framework | Avg. Jaccard | Avg. Dice | Avg. F1 | NJ2 | Rmks |
---|---|---|---|---|---|---|---|
topcoders_selim | 2D | tf1_keras | -- | -- | -- | -- | Empty outputs |
maskrcnn | 2D | tf1_keras | 0.380 | 0.504 | -- | -- | the model was pre-trained on DSB2018 with custom parameters |
deepcell | 3D | tf2 | 0.327 | 0.446 | -- | -- | selection of only the biggest labeled object |