README.md

metadata

license: cc-by-4.0
task_categories:
  - video-classification
tags:
  - medical
  - ultrasound
  - eye
  - ocular
  - classification
arxiv: 2508.04735
size_categories:
  - 1K<n<10K
language:
  - en
pretty_name: erdes
dataset_info:
  features:
    - name: clip_id
      dtype: string
    - name: file_path
      dtype: string
    - name: diagnostic_class
      dtype: string
    - name: subtype
      dtype: string
    - name: anatomical_subclass
      dtype: string
    - name: fps
      dtype: float32
    - name: frame_count
      dtype: int32
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: duration_seconds
      dtype: float32
  splits:
    - name: train
      num_examples: 5381
configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata.csv
  - config_name: non_rd_vs_rd
    data_files:
      - split: train
        path: splits/non_rd_vs_rd/train.csv
      - split: validation
        path: splits/non_rd_vs_rd/val.csv
      - split: test
        path: splits/non_rd_vs_rd/test.csv
  - config_name: macula_detached_vs_intact
    data_files:
      - split: train
        path: splits/macula_detached_vs_intact/train.csv
      - split: validation
        path: splits/macula_detached_vs_intact/val.csv
      - split: test
        path: splits/macula_detached_vs_intact/test.csv
  - config_name: normal_vs_pvd
    data_files:
      - split: train
        path: splits/normal_vs_pvd/train.csv
      - split: validation
        path: splits/normal_vs_pvd/val.csv
      - split: test
        path: splits/normal_vs_pvd/test.csv
  - config_name: normal_vs_rd
    data_files:
      - split: train
        path: splits/normal_vs_rd/train.csv
      - split: validation
        path: splits/normal_vs_rd/val.csv
      - split: test
        path: splits/normal_vs_rd/test.csv
  - config_name: pvd_vs_rd
    data_files:
      - split: train
        path: splits/pvd_vs_rd/train.csv
      - split: validation
        path: splits/pvd_vs_rd/val.csv
      - split: test
        path: splits/pvd_vs_rd/test.csv

ERDES: Eye Retinal Detachment Ultrasound Dataset

Resource	Link
Website
Paper
Checkpoints
Dataset
Code

📌 Introduction

ERDES is a large-scale, publicly available dataset of 3D ocular ultrasound videos for retinal and macular detachment classification. It was introduced in our paper ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound. The corpus consists of 5,381 expertly annotated video clips totaling 5 hours and 10 minutes, providing a valuable resource for medical AI research in ophthalmology.

Key Features:

• 5,381 labeled ultrasound video clips
• Expert annotations for retinal detachment (RD) and macular status
• Structured classification (Normal, RD, PVD, macula-detached/intact)
• Preprocessed for privacy and consistency

---

🎯 Motivation

Medical video datasets for AI are scarce despite their clinical importance. ERDES bridges this gap by offering:

• A standardized benchmark for retinal detachment classification in ultrasound videos.
• Support for spatiotemporal analysis (e.g., 3D CNNs).
• Open access to accelerate research in ocular diagnostics.

---

📊 Dataset Overview

1. Data Structure

Videos are categorized into two primary groups:

Non-RD (Non-Retinal Detachment):

• Normal
• Posterior Vitreous Detachment (PVD)

RD (Retinal Detachment):

• Macula-Detached
  • Bilateral (nasal and temporal regions involved)
  • Temporal detachment only
• Macula-Intact
  • Nasal detachment
  • Temporal detachment

2. Annotations

Each clip is labeled by sonologists for:

• Presence/absence of retinal detachment.
• Macular involvement (detached/intact).

3. Preprocessing

• Privacy: PHI removed using YOLOv8-based globe detection.
• Consistency: Cropped to the ocular ROI.
• Format: MP4 videos.

---

📥 Download

Access the dataset via the HuggingFace API:

from datasets import load_dataset

dataset = load_dataset("pcvlab/erdes")

---

🛠️ Code & Baselines

We open source our baseline experiments on our GitHub repo, which includes:

• Baseline 3D CNN and ViT models for classification.
• End-to-end diagnostic pipeline for macular detachment.

---

📜 Citation

If you use ERDES, please cite:

@article{ozkuterdes,
  title={ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound},
  author={Ozkut, Yasemin and Navard, Pouyan and Adhikari, Srikar and Situ-LaCasse, Elaine and Acu{\~n}a, Josie and Yarnish, Adrienne A and Yilmaz, Alper},
  journal={arXiv preprint arXiv:2508.04735},
  year={2025}
}