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RailNet Tooth Segmentation In CBCT Image

This model has been pushed to the Hub using the PytorchModelHubMixin integration: Hugging Face Space (available now): Code: Paper: RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT

Sourcehuggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image/tree/f00ceeb8cf84ad602a2de2000e9fb7a37833b365
KindOtherVersionRevision f00ceebBgrade

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Kind
Other
File size
36.1–801 MB
Version
vRevision f00ceeb
Updated
Oct 6, 2026
Downloads
43
Source
Hugging Face

Source description

This model has been pushed to the Hub using the PytorchModelHubMixin integration:

Steps to use our model in this repository:

  1. Clone this repository with the following command: 
    git clone https://huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image
cd railNet-tooth-segmentation-in-CBCT-image
  2. Create a virtual environment to experience our model using the following command: 
    conda create -n railnet python=3.10
conda activate railnet
pip install -r requirements.txt
python gradio_app.py
  3. In the current working directory, find the example_input_file folder.
    Select an arbitrary .h5 file in this folder and drag it into the Gradio interface for model inference.
  4. Waiting for about 1min~2min30s, the model inference is completed and the segmentation result and 3D rendering visualization will be produced.
    Both the original image and the segmentation result are saved in .nii.gz format in the output folder of the same directory.
  5. Since Gradio performs 1/2 downsampling on the 3D segmentation visualization, the segmentation accuracy is degraded.
    Users can drag the .nii.gz format files in the output folder into the ITK-SNAP software to view the accurate segmentation visualization.

Sources

1 source
Hugging FacePrimaryVersion f00ceeb8cf84ad602a2de2000e9fb7a37833b365
huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image/tree/f00ceeb8cf84ad602a2de2000e9fb7a37833b365

Verified Oct 6

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Model artifacts

10 artifacts

example_input_file/CBCT_01.h5

h5 · 684 MB · SHA-256 19680f9f0b22…8a42 · Hugging Face

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example_input_file/CBCT_02.h5

h5 · 684 MB · SHA-256 ed421541ed92…735f · Hugging Face

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example_input_file/CBCT_03.h5

h5 · 684 MB · SHA-256 4beb84cb3142…a8ae · Hugging Face

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example_input_file/CBCT_04.h5

h5 · 801 MB · SHA-256 ecfd0c239381…7a81 · Hugging Face

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model weights/rail_0_iter_7995_best.pth

pth · 36.1 MB · SHA-256 9085f5b56d75…79d3 · Hugging Face

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model weights/rail_1_iter_7995_best.pth

pth · 36.1 MB · SHA-256 66a4b9ba3314…2857 · Hugging Face

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model weights/rail_2_iter_7995_best.pth

pth · 96.6 MB · SHA-256 ef122d5b0770…4359 · Hugging Face

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model weights/rail_3_iter_7995_best.pth

pth · 96.6 MB · SHA-256 01e921070416…f85d · Hugging Face

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model weights/roi_best_model.pth

pth · 36.1 MB · SHA-256 629e02002b68…ae57 · Hugging Face

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model.safetensors

safetensors · 36.1 MB · SHA-256 20c9bfd0d3bd…a38a · Hugging Face

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Source excerpts

3 excerpts
ReadmeJul 29huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image

--- tags: - model_hub_mixin - pytorch_model_hub_mixin - medical license: apache-2.0 language: - en metrics: - Dice - Jaccard - 95HD - ASD pipeline_tag: image-segmentation library_name: pytorch --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - **Hugging Face Space** (available now): https://huggingface.co/spaces/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image - Code: https://github.com/Tournesol-Saturday/RAIL - Paper: [RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT](https://huggingface.co/papers/2505.03538) **Steps to use our model in this repository:** 1. Clone this repository with the following command: ```bash git clone https://huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image cd railNet-tooth-segmentation-in-CBCT-image ``` 3. Create a virtual environment to experience our model using the following command: ```python conda create -n railnet python=3.10 conda activate railnet pip install -r requirements.txt python gradio_app.py ``` 4. In the current working directory, find the `example_input_file` folder. Select an arbitrary `.h5` file in this folder and drag it into the `Gradio` interface for model inference. 5. Waiting for about 1min~2min30s, the model inference is completed and the segmentation result and 3D rendering visualization will be produced. Both the original image and the segmentation result are saved in `.nii.gz` format in the `output` folder of the same directory. 6. Since `Gradio` performs 1/2 downsampling on the 3D segmentation visualization, the segmentation accuracy is degraded. Users can drag the `.nii.gz` format files in the `output` folder into the `ITK-SNAP` software to view the accurate segmentation visualization.

NoteOct 6huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image

Source context: 43 downloads · 5 likes · Pipeline image-segmentation · Library pytorch · Repo Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image

NoteJul 29huggingface.co/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image

Source context: 13 downloads · 3 likes · Pipeline image-segmentation · Library pytorch · Repo Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image