Pretrained model weights for Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma (Yildiz et al., 2026).

Fundus2RNFLTNorm is a deep learning framework that predicts a personalized expected-normal retinal nerve fiber layer thickness (RNFLT) map from an OCT en face fundus image. The predicted RNFLT norm is intended to capture patient-specific retinal anatomy and can be compared with the observed RNFLT map to derive a personalized RNFLT deviation map for glaucoma research.

This repository provides the pretrained weights associated with the Fundus2RNFLTNorm project.

Code: https://github.com/Harvard-AI-and-Robotics-Lab/Fundus2RNFLTNorm

Paper: https://doi.org/10.64898/2026.05.26.26354081

Dataset Details

Dataset Description

Field	Value
Institution	Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School
Task	Personalized RNFLT normative map prediction
Input	OCT en face fundus image
Output	Predicted personalized expected-normal RNFLT map
Architecture	U-Net-based deep learning model
Framework	TensorFlow / Keras
Input resolution	256 × 256
Model weights	`octfundus_to_rnflt_model.h5`
License	CC BY-NC-ND 4.0

Authors: Elif Yildiz, Lucy Zha, Nazlee Zebardast, Min Shi, Mengyu Wang
Contact: harvardophai@gmail.com, harvardairobotics@gmail.com

Model File

File	Description
`octfundus_to_rnflt_model.h5`	Pretrained OCT en face fundus → personalized RNFLT norm prediction model

How to Download

Download from the Hugging Face website

Open Files and versions and download:

octfundus_to_rnflt_model.h5

Place the downloaded file in the checkpoint/ directory of the Fundus2RNFLTNorm GitHub repository:

Fundus2RNFLTNorm/
├── checkpoint/
│   └── octfundus_to_rnflt_model.h5
├── models/
├── samples/
├── utils/
├── infer.py
├── inference.ipynb
├── octfundus2rnflt.py
├── requirements.txt
└── train.py

Download with Hugging Face Hub

pip install -U huggingface_hub

hf download harvardairobotics/Fundus2RNFLTNorm \
  octfundus_to_rnflt_model.h5 \
  --repo-type dataset \
  --local-dir checkpoint

Usage

The released implementation uses:

Python 3.8
TensorFlow 2.4.0
OpenCV-Python 4.5.5

Clone the code repository:

git clone https://github.com/Harvard-AI-and-Robotics-Lab/Fundus2RNFLTNorm.git
cd Fundus2RNFLTNorm

Load a sample OCT en face fundus image:

from octfundus2rnflt import *
import cv2
import numpy as np

oct_fundus = cv2.resize(
    cv2.imread("samples/sample_oct_fundus.png", 0),
    (256, 256)
)

img = np.array([
    np.transpose(
        np.array([oct_fundus, oct_fundus, oct_fundus]),
        (1, 2, 0)
    )
])

mask = np.ones_like(img)

Load the pretrained model:

octfundus2rnflt = PCModel(img_rows=256, img_cols=256)

octfundus2rnflt.load(
    "checkpoint/octfundus_to_rnflt_model.h5",
    train_bn=False,
    lr=0.00001
)

Generate the predicted personalized RNFLT norm map:

pred = octfundus2rnflt.model.predict([img, mask])[0][:, :, 0]

plot_2dmap(pred, show_cup=True)

A complete example is provided in inference.ipynb in the GitHub repository.

Compatibility note: Earlier versions of the GitHub code refer to the checkpoint as octfundus_to_rnflt_new2.final.h5. If using such a version, either update the checkpoint path in the code to octfundus_to_rnflt_model.h5 or rename the downloaded file accordingly.

Study Overview

The model was developed using OCT scans and visual fields from the Massachusetts Eye and Ear Glaucoma Service. In the associated study, 10,000 OCT scans with normal visual fields were used for model training, and 8,000 additional OCT scans were used for evaluation.

The model predicts patient-specific expected-normal RNFLT maps from OCT en face fundus images. These predictions can be compared with observed RNFLT maps to generate personalized RNFLT deviation maps and support research into glaucoma structure-function relationships.

Uses

Direct Use

The pretrained weights are intended for research applications including:

Reproducing the Fundus2RNFLTNorm model without retraining
Predicting personalized expected-normal RNFLT maps from OCT en face fundus images
Generating personalized RNFLT deviation maps
Investigating glaucoma structure-function relationships
Developing and evaluating personalized OCT normative modeling methods

Out-of-Scope Use

These weights are provided for research purposes only.

They are not intended for autonomous clinical diagnosis, clinical decision-making, treatment selection, or direct patient care. The associated study was released as a preprint and should be interpreted in that context.

Training Data

The underlying clinical data are private, internal data owned by Massachusetts Eye and Ear and are not distributed through this repository.

The associated study used:

10,000 OCT scans with normal visual fields for training
8,000 OCT scans for evaluation, including 2,419 scans with normal visual fields

See the associated publication for complete cohort definitions and evaluation methodology.

Access

The weights are released under the CC BY-NC-ND 4.0 license for non-commercial research use.

The “Harvard” designation indicates that this research originated from investigators affiliated with the Department of Ophthalmology at Harvard Medical School and Massachusetts Eye and Ear. It does not imply endorsement, sponsorship, or assumption of responsibility by Harvard University or Harvard Medical School.

Citation

If you use the Fundus2RNFLTNorm code or pretrained weights, please cite:

@article{yildiz2026rnfltnorm,
  title={Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma},
  author={Yildiz, Elif and Zha, Lucy and Zebardast, Nazlee and Shi, Min and Wang, Mengyu},
  journal={medRxiv},
  year={2026},
  doi={10.64898/2026.05.26.26354081},
  url={https://pubmed.ncbi.nlm.nih.gov/42245038/}
}

Contact

For questions about the pretrained model or code, please contact:

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