| --- |
| 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) |
|
|
