Clarify model description and organize evaluation details
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README.md
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license: bsd-3-clause
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tags:
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- microscopy
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- image-segmentation
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---
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license: bsd-3-clause
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tags:
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- microscopy
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- image-segmentation
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---
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# Cytellect
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Cytellect provides fine-tuned Cellpose-SAM weights for nucleolar segmentation in microscopy images. The models build on the `cpsam_v2` checkpoint and are distributed with checkpoint hashes, processing recipes and evaluation metadata for reproducible use.
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This repository includes checkpoints at 750 and 375 training updates. Fine-tuning used human-reviewed microscopy annotations, with no synthetic images.
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## Model checkpoints
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| Version | File | SHA256 |
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|---|---|---|
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| 750 updates | `ncl/r1-step0750.pt` | `9d2c2ec06aa1aa71f36695b49b0828f82fd3cb633da2832c99a9f194d0bd4099` |
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| 375 updates | `ncl/r1-step0375.pt` | `bc5ea7f25ce9a9f96e9ff510cee20587e1328ac834a0f7f82aee0bc06156d906` |
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The corresponding JSON files contain the fixed recipe, package versions, provenance hashes and aggregate metrics. The training images use NCL staining.
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## Evaluation
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The following development-set comparison uses the same reference annotations, fixed local product preprocessing, parent-nucleus gating and postprocessing for all checkpoints. Instance matching uses an IoU threshold of 0.5.
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| Product output | TP | FP | FN | Instance F1 | Union Dice | Total area difference |
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|---|---:|---:|---:|---:|---:|---:|
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| Untouched baseline | 3 | 4 | 1 | 0.5455 | 0.8534 | -20.38% |
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| 375 updates | 4 | 2 | 0 | 0.8000 | 0.9349 | -2.69% |
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| 750 updates | 4 | 0 | 0 | 1.0000 | 0.8951 | -11.70% |
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The 750-update checkpoint achieves the highest instance F1 in this comparison; the 375-update checkpoint has the highest union Dice and closest total area agreement. These metrics describe complementary aspects of detection and segmentation.
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<details>
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<summary>Evaluation data and measurement details</summary>
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Training used 8 human-approved objects in 2 regions from one experiment. Development used 4 reference objects in one region from a separately cultured and treated experiment. The development image was excluded from training and used for checkpoint selection. The reported results are development-set measurements, not an independent final-test estimate. Training-region results are excluded from the comparison.
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At 750 updates, three objects have area errors of +5, -6 and +5 pixels. The fourth object has 360 predicted pixels versus 608 reference pixels, corresponding to 40.8% undersegmentation and IoU 0.5638. Instance F1 of 1.0 represents four matches at IoU 0.5; at IoU 0.7, instance F1 is 0.75. Boundary and area agreement are reported separately from instance detection.
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Raw model output at 750 updates has TP 3, FP 1, FN 1 and F1 0.75. The fourth object has raw IoU of approximately 0.428; fixed product postprocessing raises it above 0.5. The table above reports the processed output. Relative to 375 updates, the 750-update checkpoint removes two unmatched fragments, with lower union Dice and a larger total area difference. Training-region inspection also recorded geometry errors and a downstream parent-gate exclusion.
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The results describe the stated images and processing conditions. Performance on other staining protocols and experiments should be evaluated using corresponding reference annotations.
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</details>
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## Usage and reproducibility
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Use the input conditions and processing recipe documented in the accompanying metadata. Preserve the base model and record the exact checkpoint identity. Load these state dictionaries with a compatible Cellpose-SAM implementation and `weights_only=True`.
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The repository distributes model state-dict weights and aggregate metadata. Research images, annotations, optimizer state, raw logs, specimen identifiers and private filesystem paths are excluded.
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## License and attribution
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The upstream fixed-revision model card declares BSD-3-Clause. See `NOTICE.md` for the exact upstream revision and base checkpoint, and `LICENSE.cellpose` for the upstream copyright, conditions and disclaimer. The weights have been modified by fine-tuning; no upstream endorsement is implied.
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