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README.md

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+---
+license: mit
+library_name: cellpose
+tags:
+  - cellpose
+  - cpsam
+  - segmentation
+  - microscopy
+  - toxoplasma
+  - spacr
+pipeline_tag: image-segmentation
+---
+
+# Toxoplasma PV v1
+
+Cellpose-SAM (`cpsam`) fine-tune that segments **Toxoplasma gondii parasitophorous
+vacuoles (PVs)** in fluorescence microscopy fields.
+
+Trained on images of *Toxoplasma* tachyzoite PVs stained with **goat
+anti-Toxoplasma-biotin**, and on *Toxoplasma* tachyzoites **expressing DsRed in
+the PV lumen**.
+
+## Use in spaCR
+
+Select **Toxoplasma PV v1** as the pathogen model in the Mask module. spaCR
+downloads the checkpoint on first use. Equivalent to passing the checkpoint
+path as `pathogen_model`.
+
+```python
+from spacr.core import preprocess_generate_masks
+preprocess_generate_masks({..., "pathogen_model": "<path to cpsam_v2_toxo_r2>"})
+```
+
+## Performance
+
+Held-out NAS set (n=11 fields, 624 objects), against the stock `cpsam` weights:
+
+| metric | vanilla cpsam | this model |
+|---|---:|---:|
+| F1 @ IoU 0.5 | 0.713 | **0.867** |
+| mAP (0.5–0.9) | 0.322 | **0.595** |
+| Aggregated Jaccard | 0.426 | **0.808** |
+
+Per-IoU on the round-2 held-out set:
+
+| IoU | precision | recall | F1 | AP |
+|---|---:|---:|---:|---:|
+| 0.5 | 0.843 | 0.886 | 0.864 | 0.802 |
+| 0.7 | 0.768 | 0.808 | 0.788 | 0.658 |
+| 0.9 | 0.311 | 0.327 | 0.319 | 0.170 |
+
+Round 2 vs round 1 — round 2 bought **generalisation**, not in-domain accuracy:
+
+| set | n | round 1 F1 | round 2 F1 | Δ |
+|---|---:|---:|---:|---:|
+| NAS held-out (in-domain) | 11 | 0.8668 | 0.8641 | −0.003 |
+| curated-new (cross-condition) | 40 | 0.8625 | **0.8848** | +0.022 |
+
+The in-domain set is 11 fields, so that −0.003 is inside noise. The
+cross-condition gain is the real result (AJI 0.784 → 0.920).
+
+## Training
+
+115 image/mask pairs (104 train / 11 test), 100 epochs, base `cpsam_v2`.
+
+## Limitations
+
+Accuracy falls sharply above IoU 0.8 — boundaries are approximate, so this is
+suited to **counting and area** rather than precise morphometry. Trained on the
+two stains named above; other labels are untested.