---
license: mit
tags:
  - cellpose
  - image-segmentation
  - fluorescence-microscopy
  - bacteria
  - confocal
base_model: cellpose/cellpose-sam
---

# Cellpose-SAM fine-tuned for bacterial segmentation in mouse colon tissue

Fine-tuned weights for [Cellpose-SAM](https://github.com/MouseLand/cellpose) (`cpsam`) to segment individual bacteria in Zeiss Airyscan confocal images of mouse colon tissue sections.

## Model description

The base model (`cpsam`) was fine-tuned on manually annotated BFP-channel patches from 16-bit TIFF stacks acquired on a Zeiss LSM 900 Airyscan at 100× magnification. Images contain bacteria expressing a BFP constitutive reporter, embedded in colon tissue with fecal autofluorescence.

**Key imaging parameters:**
- Pixel size: 0.035 µm/px
- Expected bacterial diameter: ~43 px (~1.5 µm)
- Input channel: BFP (constitutive, used for segmentation only)
- Stack size: (4, 6323, 6344) uint16

## Available model runs

| Run | Train patches | Val patches | Cells annotated | AP@0.5 base | AP@0.5 fine-tuned |
|-----|--------------|-------------|-----------------|-------------|-------------------|
| `4stacks_5x5_norm` | 75 | 25 | 3,913 | 0.641 | **0.724** |
| `5stacks` | 35 | 8 | 4,641 | 0.885 | 0.842 |
| `1stack` | — | — | — | — | — |

`4stacks_5x5_norm` is the recommended model (largest annotated patch set, best AP improvement over base).

## How to use

```python
from cellpose import models
import numpy as np

# Load fine-tuned model
model = models.CellposeModel(
    pretrained_model="path/to/models/finetuned/4stacks_5x5_norm/models/cpsam_4stacks_5x5_norm",
    gpu=True
)

# bfp_norm: float32 [0, 1] normalised BFP channel (2D array)
masks, flows, styles = model.predict(
    [bfp_norm],
    diameter=43,        # round(1.5 / 0.035)
    channels=[0, 0],
    normalize=True
)
```

**Important:** always normalise the BFP channel before inference (`normalize=True` or manual p1–p99 stretch). Measure fluorescence intensities from the raw uint16 stack, not the normalised image.

## Training details

- Base model: `cpsam` (Cellpose-SAM)
- Epochs: 100
- Learning rate: 1e-5
- Weight decay: 0.1
- Batch size: 1
- Framework: Cellpose 4.x (`train.train_seg`)
- Hardware: NVIDIA GPU (CUDA 12.6)