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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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library_name: sam2 |
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pipeline_tag: image-segmentation |
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tags: |
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- whole-slide-imaging |
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- histopathology |
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- tissue-segmentation |
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- sam2 |
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--- |
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# AtlasPatch: Whole-Slide Image Tissue Segmentation |
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Segmentation model for whole-slide image (WSI) thumbnails, built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail at 1.25x magnification level (resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails. AtlasPatch codebase (WSI preprocessing & tooling): https://github.com/AtlasAnalyticsLab/AtlasPatch |
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## Quickstart |
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Install dependencies: |
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```bash |
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pip install atlas-patch |
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``` |
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Recommended: use the same components we ship in AtlasPatch. The segmentation service will (a) load your WSI with the registered backend, (b) build a 1.25× power thumbnail, (c) resize it to 1024×1024, (d) run SAM2 with a full-frame box, and (e) return a mask aligned to the thumbnail. |
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```python |
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import numpy as np |
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import torch |
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from pathlib import Path |
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from PIL import Image |
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from importlib.resources import files |
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from atlas_patch.core.config import SegmentationConfig |
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from atlas_patch.services.segmentation import SAM2SegmentationService |
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from atlas_patch.core.wsi import WSIFactory |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# 1) Config: packaged SAM2 Hiera-T config; leave checkpoint_path=None to auto-download from HF. |
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cfg_path = Path(files("atlas_patch.configs") / "sam2.1_hiera_t.yaml") |
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seg_cfg = SegmentationConfig( |
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checkpoint_path=None, # downloads Atlas-Patch/model.pth from Hugging Face |
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config_path=cfg_path, |
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device=str(device), |
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batch_size=1, |
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thumbnail_power=1.25, |
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thumbnail_max=1024, |
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mask_threshold=0.0, |
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) |
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segmenter = SAM2SegmentationService(seg_cfg) |
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# 2) Load a WSI and segment the thumbnail. |
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wsi = WSIFactory.load("slide.svs") # backend auto-detected (e.g., openslide) |
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mask = segmenter.segment_thumbnail(wsi) # mask.data matches the thumbnail size |
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# 3) Save the mask. |
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mask_img = Image.fromarray((mask.data > 0).astype(np.uint8) * 255) |
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mask_img.save("thumbnail_mask.png") |
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``` |
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## Preparing the Thumbnail |
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AtlasPatch generates thumbnails at **1.25× objective power** (power-based downsampling) and then clamps the longest side to **1024 px**. Using the same helper the library uses: |
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```python |
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from atlas_patch.core.wsi import WSIFactory |
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wsi = WSIFactory.load("slide.svs") |
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thumb = wsi.get_thumbnail_at_power(power=1.25, interpolation="optimise") |
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thumb.thumbnail((1024, 1024)) # in-place resize to 1024×1024 |
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thumb.save("thumbnail.png") |
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``` |
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## License and Commercial Use |
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This model is released under **CC-BY-NC-SA-4.0**, which strictly disallows commercial use of the model weights or any derivative works. Commercialization includes selling the model, offering it as a paid service, using it inside commercial products, or distributing modified versions for commercial gain. Non-commercial research, experimentation, educational use, and use by academic or non-profit organizations is permitted under the license terms. If you need commercial rights, please contact the authors to obtain a separate commercial license. See the LICENSE file in this repository for full terms. |
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## Citation |
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If you use this model, please cite SAM2 and the AtlasPatch project. A formal paper is forthcoming. |
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