README.md

---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- biology
- histology
- histopathology
---

# Cellpose Model for High-Grade Serous Ovarian Cancer Nuclei Segmentation

# Model
- **cellseg_models.pytorch** implementation of **Cellpose**: [https://www.nature.com/articles/s41592-020-01018-x](https://www.nature.com/articles/s41592-020-01018-x)
- Backbone encoder: pre-trained **efficientnet_b5** from pytorch-image-models [https://github.com/huggingface/pytorch-image-models](https://github.com/huggingface/pytorch-image-models)


# USAGE

## 1. Install cellseg_models.pytorch and albumentations
```
pip install cellseg-models-pytorch
pip install albumentations
```

## 2. Load trained model
```python
from cellseg_models_pytorch.models.cellpose import CellPose

model = CellPose.from_pretrained("hgsc_v1_efficientnet_b5")
```

## 3. Run inference for one image
```python
from albumentations import Resize, Compose
from cellseg_models_pytorch.utils import FileHandler
from cellseg_models_pytorch.transforms.albu_transforms import MinMaxNormalization

model.set_inference_mode()

# Resize to multiple of 32 of your own choosing
transform = Compose([Resize(1024, 1024), MinMaxNormalization()])

im = FileHandler.read_img(IMG_PATH)
im = transform(image=im)["image"]

prob = model.predict(im)
out = model.post_process(prob)
# out = {"nuc": [(nuc instances (H, W), nuc types (H, W))], "cyto": None, "tissue": None}
```

## 3.1 Run inference for image batch
```python
import torch
from cellseg_models_pytorch.utils import FileHandler

model.set_inference_mode()

# dont use random matrices IRL
batch = torch.rand(8, 3, 1024, 1024)

prob = model.predict(im)
out = model.post_process(prob)
# out = {
#  "nuc": [
#    (nuc instances (H, W), nuc types (H, W)),
#    (nuc instances (H, W), nuc types (H, W)),
#    .
#    .
#    .
#    (nuc instances (H, W), nuc types (H, W))    
#  ],
#  "cyto": None,
#  "tissue": None
#}
```

## 4. Visualize output
```python
from matplotlib import pyplot as plt
from skimage.color import label2rgb

fig, ax = plt.subplots(1, 3, figsize=(18, 6))
ax[0].imshow(im)
ax[1].imshow(label2rgb(out["nuc"][0][0], bg_label=0)) # inst_map
ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
```
![out](cpose_out.png)

## Dataset Details
Semi-manually annotated HGSC Primary Omental samples from the (private) DECIDER cohort. Data acquired in the DECIDER project, 
funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193.

**Contains:**
- 198 varying sized image crops at 20x magnification.
- 98 468 annotated nuclei

## Dataset classes

```
nuclei_classes = {
    0: "background",
    1: "neoplastic",
    2: "inflammatory",
    3: "connective",
    4: "dead",
    5: "macrophage_cytoplasm",
    6: "macrophage_nucleus",
}
```

## Dataset Class Distribution
- connective nuclei: 46 100 (~47%)
- neoplastic nuclei: 22 761 (~23%)
- inflammatory nuclei 19 185 (~19%)
- dead nuclei 1859 (~2%)
- macrophage nuclei and cytoplasms: 4550 (~5%)

# Model Training Details:
First, the image crops in the training data were tiled into 224x224px patches with a sliding window (stride=32px). 

Rest of the training procedures follow this notebook: [link]

# Citation

cellseg_models.pytorch:
```
@misc{https://doi.org/10.5281/zenodo.12666959,
  doi = {10.5281/ZENODO.12666959},
  url = {https://zenodo.org/doi/10.5281/zenodo.12666959},
  author = {Okunator,  },
  title = {okunator/cellseg_models.pytorch: v0.2.0},
  publisher = {Zenodo},
  year = {2024},
  copyright = {Creative Commons Attribution 4.0 International}
}
```

Cellpose original paper:
```
@article{Stringer2020,
  title = {Cellpose: a generalist algorithm for cellular segmentation},
  volume = {18},
  ISSN = {1548-7105},
  url = {http://dx.doi.org/10.1038/s41592-020-01018-x},
  DOI = {10.1038/s41592-020-01018-x},
  number = {1},
  journal = {Nature Methods},
  publisher = {Springer Science and Business Media LLC},
  author = {Stringer,  Carsen and Wang,  Tim and Michaelos,  Michalis and Pachitariu,  Marius},
  year = {2020},
  month = dec,
  pages = {100–106}
}
```

## Licence
These model weights are released under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at:

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

## Additional Terms

While the Apache 2.0 License grants broad permissions, we kindly request that users adhere to the following guidelines:
Medical or Clinical Use: This model is not intended for use in medical diagnosis, treatment, or prevention of disease of real patients. It should not be used as a substitute for professional medical advice.