| --- |
| title: Scratch Assay Segmentation |
| emoji: 🔬 |
| colorFrom: indigo |
| colorTo: gray |
| sdk: streamlit |
| sdk_version: 1.60.0 |
| app_file: app.py |
| pinned: false |
| license: agpl-3.0 |
| models: |
| - nmariotto/scratch-assay-segmentation |
| --- |
| |
| # Scratch Assay Segmentation Tool |
|
|
| Automated segmentation of the cell-free gap in brightfield scratch (wound-healing) assay |
| images, and measurement of its area. |
|
|
| Companion to *"Deep Learning Instance Segmentation for Quantitative Analysis of Cell |
| Migration in Wound Healing Assays"* (Mariotto et al., Cytometry Part A, under revision). |
|
|
| ## What it does |
|
|
| Upload one image or a folder. The tool returns the segmented gap, its area in pixels² and |
| — if you supply a scale — in µm², and the fraction of the field it occupies. Results |
| export as a table, an overlay image and the contour coordinates. |
|
|
| Two model scales are offered. They differ **only** in size: initialisation, padding and |
| training schedule are identical, and across the five configurations evaluated in the study |
| no pairwise difference in mean Average Precision is distinguishable. The choice is latency |
| against recall, not accuracy. |
|
|
| | | mAP@50 | Recall | CPU | |
| |---|---|---|---| |
| | **M** — default | 93.4 ± 1.1% | 78.3 ± 3.0% | ~345 ms | |
| | **S** — fast mode | 94.0 ± 0.7% | 74.3 ± 2.3% | ~174 ms | |
|
|
| Mean ± SD over five training seeds on a held-out test set of 234 images. |
|
|
| ## What it is not for |
|
|
| Agreement with a careful manual measurement has 95% limits of agreement of roughly ±0.3 in |
| closure fraction. **A single automated measurement is not a substitute for a single manual |
| one.** The tool is for comparing conditions across many wells. |
|
|
| Below about 5% of the field the gap becomes hard to delineate and agreement degrades; the |
| interface flags those measurements. |
|
|
| ## Weights |
|
|
| Downloaded at run time from <https://huggingface.co/nmariotto/scratch-assay-segmentation> |
| (`M.pt`, `S.pt`). The repository is public and needs no token, so the exact file behind any |
| prediction can be retrieved, checked and redeployed independently. |
|
|
| ## Licence |
|
|
| **AGPL-3.0.** This application and the weights it serves derive from Ultralytics YOLO11, |
| which is AGPL-3.0; no commercial licence was obtained. The image dataset is released |
| separately under CC BY 4.0 and the statistical analysis code under MIT. |
|
|
| ## Links |
|
|
| - Data and code archive: <https://doi.org/10.5281/zenodo.20298129> |
| - Source: <https://github.com/nykemariotto/scratch-assay-segmentation> |
|
|
| Developed by the Medical Physics Laboratory, Department of Biophysics and Pharmacology, |
| IBB — UNESP. FAPESP 2024/01849-4. Coordination: Prof. Allan Alves. Development: Nycolas |
| Mariotto. |
