fidoptInstanSeg
InstanSeg's released nuclei models, brightfield (trained on TNBC, LyNSeC, NuInsSeg, IHC TMA and CoNSeP) or fluorescence (CPDMI 2023) as the image calls for, run as the package's README runs them, every nucleus one label.
SegmentationCPUversion instanseg-torch 0.1.2, models brightfield_nuclei and fluorescence_nuclei_and_cells 0.1.1 of release instanseg_models_v0.1.1 (sha256 427aa9cf, d5289ce1)HISTOLOGY, MICROSCOPYApache-2.0
Run it
With its KonfAI app, at the benchmark's settings (the fidopt app repositories are private for now: a Hugging Face token with access opens them):
pip install konfai-apps konfai-apps infer fideus-labs/fidopt-segmentation:InstanSeg -i image.nii.gz -o out
The app on Hugging Face: its model, its settings and how it is scored.
Where it works
Its place on each kind of problem it has results on, among the methods ranked there. A method that leads one problem can trail another: read the one closest to yours.
| Problem | Place | Ranked | Datasets |
|---|---|---|---|
| Cell nuclei on histology and fluorescence microscopy, 2D segmentation | not ranked yet | 1 of 2 |
Results
| Dataset | # | Read by | Score | Failed | Time s |
|---|---|---|---|---|---|
| Data Science Bowl 2018, nuclei of many experiments | 2 | Dice | 0.371 | 0 | 12 |
- Reference
- Goldsborough et al., arXiv 2408.15954, 2024; Goldsborough et al., bioRxiv 2024.09.04.611150