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Fine-tuned models for semi-automated segmentation of mouse skin layers and hair follicles from H&E histology images. Part of the dpcim pipeline.
Model source
Source description
Fine-tuned models for semi-automated segmentation of mouse skin layers and hair follicles from H&E histology images. Part of the dpc_im pipeline.
Sources
1 sourceVerified Aug 18
Model artifacts
1 artifactSource excerpts
2 excerpts| File | Task | Base model | Training images |
|---|
best_model.pth | 5-class layer segmentation (background, dermis, epidermis, adipose, muscle) | U-Net ResNet34 — ImageNet | 5 annotated mouse skin H&E images (1920×1440 px, 4×, 7.615 μm/px) |
cellpose_hf_best | Hair follicle instance segmentation | Cellpose SAM | 8 train + 2 val annotated mouse skin H&E images (1920×1440 px, 4×, 7.615 μm/px) |
See dpc_im for installation and full pipeline usage.
Pachitariu, M., Rariden, M., & Stringer, C. (2025). Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv. https://doi.org/10.1101/2025.04.03.647135
Iakubovskii, P. (2019). Segmentation Models Pytorch. GitHub. https://github.com/qubvel/segmentation_models.pytorch
--- license: mit tags: - image-segmentation - histology - biology - unet - cellpose --- # dpc_im — Mouse Skin H&E Segmentation Models Fine-tuned models for semi-automated segmentation of mouse skin layers and hair follicles from H&E histology images. Part of the [dpc_im](https://github.com/SolomonLalala/dpc_im) pipeline. ## Models | File | Task | Base model | Training images | |---|---|---|---| | `best_model.pth` | 5-class layer segmentation (background, dermis, epidermis, adipose, muscle) | [U-Net ResNet34](https://github.com/qubvel/segmentation_models.pytorch) — ImageNet | 5 annotated mouse skin H&E images (1920×1440 px, 4×, 7.615 µm/px) | | `cellpose_hf_best` | Hair follicle instance segmentation | [Cellpose SAM](https://github.com/MouseLand/cellpose) | 8 train + 2 val annotated mouse skin H&E images (1920×1440 px, 4×, 7.615 µm/px) | ## Usage See [dpc_im](https://github.com/SolomonLalala/dpc_im) for installation and full pipeline usage. ## Citation Pachitariu, M., Rariden, M., & Stringer, C. (2025). Cellpose-SAM: superhuman generalization for cellular segmentation. *bioRxiv*. https://doi.org/10.1101/2025.04.03.647135 Iakubovskii, P. (2019). Segmentation Models Pytorch. GitHub. https://github.com/qubvel/segmentation_models.pytorch
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo solomonlalala/dpc_im
