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FunduSegmenter

This is the official repository for FunduSegmenter, the first adaptation of the RETFound foundation model for joint optic disc (OD) and optic cup (OC) segmentation in retinal fundus images.

Sourcehuggingface.co/JusticeZzy/FunduSegmenter/tree/2ba4d2623126c5ecee903c07814516240fea8050
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File size
7.69 MB–1.53 GB
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vRevision 2ba4d26
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Oct 8, 2026
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Hugging Face

Source description

FunduSegmenter

This is the official repository for FunduSegmenter, the first adaptation of the RETFound foundation model for joint optic disc (OD) and optic cup (OC) segmentation in retinal fundus images.

Introduction

FunduSegmenter integrates a series of novel modules with RETFound, including a Pre-adapter, a Decoder, a Post-adapter, skip connections with Convolutional Block Attention Module (CBAM), and a Vision Transformer block adapter. The model demonstrates strong stability and generalization on both in-distribution and out-of-distribution data, providing stable OD and OC segmentation across multiple fundus camera datasets (IDRiD, Drishti-GS, RIM-ONE-r3, and REFUGE).

Repository Contents

This repository hosts the pre-trained weights and datasets associated with the paper:

  1. Datasets.zip: Processed datasets used in the study.
  2. DUNet_OD_CentreCrop_pretrained.pth: Pre-trained DUNet used for OD center cropping pre-processing.
  3. FunduSegmenter_CroppedImage.pth: Pre-trained FunduSegmenter trained on cropped ROI images.
  4. FunduSegmenter_OriginalImage.pth: Pre-trained FunduSegmenter trained on original fundus images.
  5. Weights.zip: All trained weights reported in the paper's experiments.
  6. mobilenet_v2-6a65762b.pth: Required pre-trained weight for the DoFE baseline.

License Note

While the code in the GitHub repository is under the MIT License, all the weights (including the original RETFound weights and the derived FunduSegmenter weights) are under CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International). This means the weights are for non-commercial use only.

Citation

If you use our code or weights, please cite the following paper:

@article{zhao2026fundusegmenter,
    author = {Zhao, Zhenyi and Mookiah, Muthu Rama Krishnan and Trucco, Emanuele},
    title = {FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images},
    journal = {Translational Vision Science & Technology},
    volume = {15},
    number = {5},
    pages = {14-14},
    year = {2026},
    month = {05}
}

Contact

Zhenyi Zhao: [email protected] or [email protected]

Sources

1 source
Hugging FacePrimaryVersion 2ba4d2623126c5ecee903c07814516240fea8050
huggingface.co/JusticeZzy/FunduSegmenter/tree/2ba4d2623126c5ecee903c07814516240fea8050

Verified Aug 25

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Model artifacts

4 artifacts

DUNet_OD_CentreCrop_pretrained.pth

pth · 7.69 MB · SHA-256 a4c0fe83f698…5588 · Hugging Face

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FunduSegmenter_CroppedImage.pth

pth · 1.53 GB · SHA-256 87a3a481bd2b…6600 · Hugging Face

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FunduSegmenter_OriginalImage.pth

pth · 1.53 GB · SHA-256 ee169eba355e…6d53 · Hugging Face

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mobilenet_v2-6a65762b.pth

pth · 8.66 MB · SHA-256 6a65762b84bb…7a8b · Hugging Face

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Source excerpts

2 excerpts
ReadmeAug 25huggingface.co/JusticeZzy/FunduSegmenter

--- license: cc-by-nc-4.0 pipeline_tag: image-segmentation library_name: pytorch tags: - medical-imaging - foundation models - fine-tuning - retina - fundus - optic disc - optic cup - domain generalization --- # FunduSegmenter This is the official repository for **FunduSegmenter**, the first adaptation of the [RETFound](https://github.com/rmaphoh/RETFound) foundation model for joint optic disc (OD) and optic cup (OC) segmentation in retinal fundus images. - **Paper:** [FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images](https://doi.org/10.1167/tvst.15.5.14) - **Code:** [GitHub - JusticeZzy/FunduSegmenter](https://github.com/JusticeZzy/FunduSegmenter) - **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Non-commercial use only) ## Introduction FunduSegmenter integrates a series of novel modules with RETFound, including a Pre-adapter, a Decoder, a Post-adapter, skip connections with Convolutional Block Attention Module (CBAM), and a Vision Transformer block adapter. The model demonstrates strong stability and generalization on both in-distribution and out-of-distribution data, providing stable OD and OC segmentation across multiple fundus camera datasets (IDRiD, Drishti-GS, RIM-ONE-r3, and REFUGE). ## Repository Contents This repository hosts the pre-trained weights and datasets associated with the paper: 1. **Datasets.zip**: Processed datasets used in the study. 2. **DUNet_OD_CentreCrop_pretrained.pth**: Pre-trained DUNet used for OD center cropping pre-processing. 3. **FunduSegmenter_CroppedImage.pth**: Pre-trained FunduSegmenter trained on cropped ROI images. 4. **FunduSegmenter_OriginalImage.pth**: Pre-trained FunduSegmenter trained on original fundus images. 5. **Weights.zip**: All trained weights reported in the paper's experiments. 6. **mobilenet_v2-6a65762b.pth**: Required pre-trained weight for the DoFE baseline. ## License Note While the code in the GitHub repository is under the MIT License, all the weights (including the original RETFound weights and the derived FunduSegmenter weights) are under **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International). This means the weights are for **non-commercial use only**. ## Citation If you use our code or weights, please cite the following paper: ```bibtex @article{zhao...

NoteAug 25huggingface.co/JusticeZzy/FunduSegmenter

Source context: 0 downloads · 2 likes · Pipeline image-segmentation · Library pytorch · Repo JusticeZzy/FunduSegmenter