Upload README.md

README.md

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 ---
 license: agpl-3.0
+tags:
+  - image-segmentation
+  - instance-segmentation
+  - ultralytics
+  - yolo
+  - cell-biology
+  - microscopy
+  - wound-healing-assay
+library_name: ultralytics
+pipeline_tag: image-segmentation
 ---
+
+# Scratch-assay wound segmentation — YOLO11-seg
+
+Instance-segmentation weights for the cell-free gap in brightfield scratch (wound-healing)
+assay images. Two scales are provided: `M.pt` is the default and `S.pt` is a faster
+alternative that trades about four percentage points of recall for roughly half the CPU
+latency.
+
+These weights accompany the manuscript *"Deep Learning Instance Segmentation for
+Quantitative Analysis of Cell Migration in Wound Healing Assays"* (Cytometry Part A, under
+revision).
+
+## ⚠️ These are not the weights of the originally submitted version
+
+The first submission evaluated models trained on a dataset partitioned at the **image**
+level, so frames of the same acquisition field appeared in both training and test sets. Every
+performance figure in that version was optimistic. The dataset was rebuilt and partitioned at
+the level of the **physical acquisition field**, and all configurations were retrained. The
+weights here are from the corrected partition. The superseded weights remain available in the
+earlier deposit, marked as such; they should not be used.
+
+## Files
+
+| File | Scale | Parameters | Size | CPU (ms/img) | GPU (ms/img) | mAP@50 | Recall |
+|---|---|---|---|---|---|---|---|
+| `M.pt` | YOLO11m-seg | 22.4 M | 45.2 MB | 345 | 92 | 93.40 ± 1.08 | 78.34 ± 2.99 |
+| `S.pt` | YOLO11s-seg | 10.1 M | 20.5 MB | 174 | 85 | 93.98 ± 0.74 | 74.32 ± 2.30 |
+
+Accuracy is mean ± SD over five training seeds on a held-out test set of 234 images from 37
+acquisition groups. Latency is the median over 40 images, CPU on 16 cores, GPU on an NVIDIA
+GeForce RTX 4060 Ti. Both weights are the seed-42 checkpoint.
+
+**The two scales are not statistically distinguishable in mAP.** Across all five
+configurations evaluated, mean mAP@50 spans 93.3–94.0% and none of the ten pairwise
+differences has a cluster-bootstrap confidence interval excluding zero. The reason to prefer
+`M` is recall at the deployed operating point, not accuracy in aggregate.
+
+## Training
+
+| | |
+|---|---|
+| Architecture | YOLO11-seg (Ultralytics 8.4.102) |
+| Initialisation | COCO checkpoint |
+| Input | 640 × 640, letterbox with black padding |
+| Schedule | 100 epochs, batch 4, early stopping disabled |
+| Augmentation | HSV 0.015/0.7/0.4, translate 0.1, scale 0.5, fliplr 0.5, mosaic (off for the last 10 epochs) |
+| Framework | PyTorch 2.6.0, CUDA 12.4 |
+| Seeds | 42–46; the checkpoint published here is seed 42 |
+
+Training data: 932 images (197 validation, 234 held-out test) of HUVEC and SKOV-3 monolayers,
+brightfield, 5× objective, single laboratory. One class, `wound`.
+
+## Usage
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("M.pt")                  # or S.pt for the fast variant
+r = model.predict("scratch.png", conf=0.80, retina_masks=True)
+mask = r[0].masks                     # polygon and binary mask of the gap
+```
+
+`conf=0.80` is the operating point at which the reported precision and recall were measured
+and the default of the companion web interface.
+
+## What these weights are good for, and where they fail
+
+Agreement with a supervised reference standard, over 97 paired observations from both cell
+lines: Pearson r 0.820 ± 0.042, Lin's concordance correlation coefficient 0.803 ± 0.049, mean
+bias +0.053 ± 0.018 in the closure fraction, 95% limits of agreement −0.288 to +0.395.
+
+Read that last number carefully. **A single automated measurement can differ from a careful
+manual one by about thirty percentage points of closure.** The workflow is suitable for
+comparing conditions across many wells; it is not a substitute for manual measurement of an
+individual well.
+
+Two known limits:
+
+- **Small wounds.** Performance is governed by wound size rather than elapsed time. Above 10%
+  of the field the model agrees closely with the reference standard; between 2% and 5% mean
+  intersection over union falls to 0.362. Measurements taken while the residual gap is below
+  roughly 5% of the field are the least reliable part of a series.
+- **Isolated cells.** The gap is quantified as the region enclosed by the segmented contour,
+  so cells that detach and migrate individually into an otherwise continuous gap are not
+  subtracted from it.
+
+The ceiling is not the architecture. On blinded repeat corrections the human observer
+reproduced their own delineation at a median intersection over union of 0.861 — about what
+the models achieve. What limits agreement here is the reproducibility of the wound boundary
+itself.
+
+Acquisition envelope: one inverted microscope, brightfield, 5× objective, one institution. No
+external testing was performed. Robustness to other microscopes, magnifications or contrast
+modalities is unknown.
+
+## Licence
+
+**AGPL-3.0.** These weights are trained with Ultralytics YOLO11, which is AGPL-3.0, and no
+commercial licence was obtained; the trained weights are a derivative work and carry the same
+licence. The image dataset is released separately under CC BY 4.0, and the statistical
+analysis code under MIT.
+
+## Citation
+
+Data and code archive: https://doi.org/10.5281/zenodo.20298129
+Code repository: https://github.com/nykemariotto/scratch-assay-segmentation
+Web interface: https://huggingface.co/spaces/nmariotto/Scratch-assay-segmentation
+
+Contact: Allan F. F. Alves — allan.alves@unesp.br — ORCID 0000-0002-0954-9919