| --- |
| 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 The superseded weights remain available in the |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 |
|
|
