---
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