VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction

VAMPIRE, Vessel-Aware Mamba-based Prediction model with Informative Enhancement, is a novel multi-purpose paradigm of CVD risk assessment that jointly performs CVD risk and CVD-related condition prediction, aligning with clinical experiences.

Updates

• OCTA-CVD dataset is released here.

Overview

VAMPIRE extracts crucial vascular characteristics through two key components:

• a Mamba-Based Directional (MBD) Module that captures fine-grained vascular trajectory features
• an Information-Enhanced Morphological (IEM) Module that incorporates comprehensive vessel morphology knowledge.

Data Preparation

Download OCTA-CVD

OCTA-CVD can be downloaded at link. After downloading the data, move them into data directory. The structure should be like the following:

.
└── data/
    ├── OCTA-Enface/
    │   ├── Choriocapillaris
    │   ├── Deep
    │   └── Superficial
    └── Label/
        └── folds_info

Vessel Direction Traverse

• Setup

  SAM-OCTA is employed to generate initial vessel maps. Please set up the environment accordingly and download the pretrained weights to vessel_traverse/sam_weights.

• Segmentation

  cd vessel_traverse
  python test_sam_octa.py

  Then, the vessel segmentation map would be saved into seg directory.

• Patch Ordering

  python process_mask.py

  Then, we can obtain img2order.pkl, which records the traverse order for each OCTA scan.

Vessel Morphology Description

We first employ a classification model trained on the OCTA-500 dataset to identify potential retinal diseases.

Subsequently, we prompt GPT-4o with the diagnostic results to generate descriptions on possible vascular morphologies. The prompt can be referred to vessel_descrp/disease_prompts.json.

Our generated description can be found in vessel_descrp/p2res_disease.json.

Environment Setup

• Create Environment

  conda create -n vampire python=3.9.21 -y
  conda activate vampire
  pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
  pip install numpy==1.21.6
  pip install scikit-learn==1.2.2
  pip install transformers==4.47.1

• Install Mamba

  Mamba requirements causal_conv1d and mamba-1p1p1 are built from Vim

• Prepare pretrained weights

  We use pretrained fundus weights from VisionFM. Please first download the weights and save into the pretrain directory.

Model Training

Run training with finetune.py, and the evaluation will be executed sequentially after training.

python finetune.py

Citation

@InProceedings{
  VAMPIRE_MICCAI2025,
  author = { Wang, Lehan AND Wang, Hualiang AND Ou, Chubin AND Chen, Lushi AND Liang, Yunyi AND Li, Xiaomeng },
  title = { { VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction } }, 
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
  year = {2025},
  publisher = {Springer Nature Switzerland},
  volume = { LNCS 15974 },
  month = {October},
  pages = { 649 -- 659 },
}