1) What AutoMorph does

AutoMorph is an open pipeline for color fundus photographs that generates quantitative retinal phenotypes.

• M0: preprocessing
• M1: image quality grading
• M2: vessel, artery-vein, and disc-cup segmentation
• M3: feature extraction for disc-centred, macular Zone B/C, and whole-image regions

2) Local setup (official pattern)

Use the maintainer guide: LOCAL.md.

conda update conda
conda create -n automorph python=3.11 -y
conda activate automorph
git clone https://github.com/rmaphoh/AutoMorph.git
cd AutoMorph

conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia -y
pip install --ignore-installed certifi
pip install -r requirement.txt
pip install efficientnet_pytorch==0.7.1 --no-deps

Place images in images/, provide resolution_information.csv, then run:

sh run.sh

3) HPC/SSH setup (recommended for scale)

git clone https://github.com/rmaphoh/AutoMorph.git
cd AutoMorph
conda create -n automorph python=3.11 -y
conda activate automorph
pip install -r requirement.txt

Keep data outside the repository:

mkdir -p $HOME/retina_all/images
export AUTOMORPH_DATA=$HOME/retina_all

Generate spacing file for micron calibration:

python generate_resolution.py 0.008

Run detached over SSH:

nohup bash run.sh > "$AUTOMORPH_DATA/automorph_run_$(date +%F_%H%M).log" 2>&1 & disown
tail -f "$AUTOMORPH_DATA"/automorph_run_*.log

4) Expected outputs and verification

$AUTOMORPH_DATA/
├── images/
├── resolution_information.csv
└── Results/
    ├── M1/
    │   ├── results_ensemble.csv
    │   └── Good_quality/
    ├── M2/
    │   ├── Vessel_binary/
    │   ├── Artery_vein/
    │   └── Disc_cup/
    └── M3/
        ├── Disc_centred/*.csv
        ├── Macular_centred/
        │   ├── Macular_Zone_B_Measurement.csv
        │   └── Macular_Zone_C_Measurement.csv
        └── Whole_image/*.csv

ls -1 "$AUTOMORPH_DATA/Results/M1/Good_quality" | wc -l
find "$AUTOMORPH_DATA/Results/M2" -maxdepth 2 -type f | head -n 20
find "$AUTOMORPH_DATA/Results/M3" -maxdepth 2 -type f -name '*.csv' | sort

5) Reproducibility checklist

• AutoMorph commit hash and run date
• Python, PyTorch, CUDA versions
• GPU model and driver details
• AUTOMORPH_DATA path used at runtime
• Resolution source used in resolution_information.csv
• Total input images and M1 gradable count

6) Copy-ready methods text