| --- |
| license: agpl-3.0 |
| library_name: monai |
| pipeline_tag: image-segmentation |
| tags: |
| - pytorch |
| - unet |
| - 3d-segmentation |
| - microscopy |
| - light-sheet-microscopy |
| - neuroscience |
| - axon-segmentation |
| - synthetic-data |
| - domain-randomization |
| metrics: |
| - dice |
| --- |
| |
| # AxonSynth |
|
|
| Pretrained three-class 3D U-Net for axon segmentation in light-sheet microscopy, trained on domain-randomized synthetic images. |
|
|
| **Code, environment, and documentation:** [github.com/lincbrain/axonsynth](https://github.com/lincbrain/axonsynth) |
|
|
| ## Model |
|
|
| - **Architecture:** MONAI 3D U-Net, approximately 4.8 million parameters. |
| - **Classes:** background, axon shell, and axon interior. |
| - **Training patches:** 128 × 128 × 128 voxels at 0.8 µm isotropic resolution. |
| - **Checkpoint:** `best_model.pt`, the main three-class model selected at epoch 136. |
|
|
| Binary axon foreground probability is calculated by summing the shell and interior probabilities. |
|
|
| ## Download |
|
|
| Using the Hugging Face CLI: |
|
|
| ```bash |
| hf download edziocodes/axonsynth best_model.pt --local-dir weights |
| ``` |
|
|
| ## Run inference |
|
|
| Clone the [code repository](https://github.com/lincbrain/axonsynth) and create its environment: |
|
|
| ```bash |
| conda env create -f environment.yml |
| conda activate axonsynth |
| ``` |
|
|
| From the code repository root, run inference on a volume prepared at **0.8 µm isotropic resolution in model axis order**. See the repository README for preparation instructions. |
|
|
| ```bash |
| python inference/infer_lsm.py \ |
| --input prepared_0p8um.nii.gz \ |
| --checkpoint weights/best_model.pt \ |
| --output-dir predictions \ |
| --segmentation-mode three_class_shell_interior \ |
| --stitch-device cpu \ |
| --output-profile binary_focused \ |
| --threshold 0.5 \ |
| --require-cuda |
| ``` |
|
|
| This command saves the normalized input, foreground probability, binary mask, and inference settings. The threshold is configurable. |
|
|
| ## Evaluation |
|
|
| To compare a prediction against a dense ground-truth volume of the same shape, run from the code repository root: |
|
|
| ```bash |
| python tools/evaluate_lsm_case.py \ |
| --predictions-dir predictions \ |
| --prefix prepared_0p8um \ |
| --gt-path ground_truth.nii.gz \ |
| --threshold 0.5 |
| ``` |
|
|
| This writes `metrics.json`, `summary.txt`, and true-positive/false-positive/false-negative overlay images to `evaluation/`. The underlying reusable metric functions are in [`paper_analysis/metrics.py`](https://github.com/lincbrain/axonsynth/blob/main/paper_analysis/metrics.py). |
|
|
| ## License |
|
|
| This model checkpoint is released under the **GNU Affero General Public License v3.0 (AGPL-3.0)**. See [LICENSE](LICENSE) for the full terms. The source code is available in the [AxonSynth GitHub repository](https://github.com/lincbrain/axonsynth) under the same license. |
|
|
