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Running on Zero
RootScope v4: manuscript model (LightGBM per round, 3 seeds, layer + soft-neighbor context, radial prior)
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| title: 'RootScope: Cross-species Root Cell-Type Classification from Confocal Microscopy Images' | |
| emoji: 🌱 | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Classify root cell types across species from confocal TIFFs | |
| # RootScope web app | |
| Web front end for [RootScope](https://github.com/ct-tranchau/Rootscope). | |
| Upload a confocal root cross-section TIFF and get back a labeled overlay and a | |
| per-cell CSV. Every cell is segmented with Cellpose-SAM and assigned to one of | |
| nine cell types: root cap, epidermis, exodermis, cortex, endodermis, | |
| pericycle, stele, xylem, phloem. | |
| ## How to use | |
| 1. Upload a raw confocal TIFF (not a mask). Pixel size is read from the OME | |
| metadata. | |
| 2. Optionally type the species name. It is not required; if given, it is | |
| checked against the species table shipped with the package. | |
| 3. Press Run. Segmentation and the DINOv2 embeddings run on the GPU; a | |
| 700x700 image with about 200 cells takes about 20 to 45 seconds. | |
| 4. Download the overlay image and the CSV of per-cell predictions. | |
| One image at a time. For batches use the command line: `rootscope --tif-dir`. | |
| ## Run it locally | |
| ```bash | |
| conda activate rootscope | |
| pip install gradio | |
| python webapp/app.py | |
| ``` | |
| Model weights download from [ct-tranchau/Rootscope](https://huggingface.co/ct-tranchau/Rootscope) | |
| on first use. | |
| ## Author | |
| Tran Chau (tnchau@vt.edu) | |