Datasets:

pnavard
/

erdes

license: mit
task_categories:
  - video-classification
tags:
  - medical
  - ultrasound
  - eye
  - ocular
  - classification
size_categories:
  - 1K<n<10K
language:
  - en
pretty_name: erdes
dataset_info:
  features:
    - name: file_name
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': Non_RD
            '1': Macula_Intact
            '2': Macula_Detached
            '3': PVD
    - name: duration (s)
      dtype: float32
    - name: fps
      dtype: float32
  splits:
    - name: train
      num_bytes: 220137
      num_examples: 5381
  download_size: 538819
  dataset_size: 220137
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/train.csv

ERDES: Eye Retinal Detachment Ultrasound Dataset

📌 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 to 5 hours and 10 minutes, providing a valuable resource for medical AI research in ophthalmology.

Key Features:

✅ 5381 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:

Normal
Retinal Detachment (RD)
- Macula-detached:
  - Bilateral
  - Temporal detachment
- Macula-intact:
  - Nasal
  - Temporal detachment
Posterior Vitreous Detachment (PVD)

Folder structure reflects labels:

2. Annotations

Each clip is labeled by sonologists for:

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

3. Preprocessing

Privacy: PHI removed using YOLO-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("pnavard/erdes")

🛠️ Code & Baselines

Repository: we open source our baseline experimetns 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, cite:

@inproceedings{navardocular,
  title={A Benchmark Dataset for Retinal Detachment Classification in Spatiotemporal Ocular Ultrasound},
  author={Navard, Pouyan and Ozkut, Yasemin and Adhikari, Srikar and Yilmaz, Alper},
  booktitle={Nature Scientific Data (Under Review)},
  year={2025}
}