metadata

license: mit
size_categories:
  - 10K<n<100K
pretty_name: 'OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics'
tags:
  - medical
dataset_info:
  - config_name: biomarker_detection
    features:
      - name: Image
        dtype: image
      - name: B1
        dtype: float64
      - name: B2
        dtype: float64
      - name: B3
        dtype: float64
      - name: B4
        dtype: float64
      - name: B5
        dtype: float64
      - name: B6
        dtype: float64
      - name: BCVA
        dtype: float64
      - name: CST
        dtype: float64
      - name: Eye_ID
        dtype: float64
      - name: Patient_ID
        dtype: float64
    splits:
      - name: train
        num_bytes: 15852565958.136
        num_examples: 78822
      - name: test
        num_bytes: 968486633.741
        num_examples: 3871
    download_size: 15923453393
    dataset_size: 16821052591.876999
  - config_name: disease_classification
    features:
      - name: Image
        dtype: image
      - name: Scan (n/49)
        dtype: int64
      - name: Atrophy / thinning of retinal layers
        dtype: int64
      - name: Disruption of EZ
        dtype: int64
      - name: DRIL
        dtype: int64
      - name: IR hemorrhages
        dtype: int64
      - name: IR HRF
        dtype: int64
      - name: Partially attached vitreous face
        dtype: float64
      - name: Fully attached vitreous face
        dtype: float64
      - name: Preretinal tissue/hemorrhage
        dtype: float64
      - name: Vitreous debris
        dtype: float64
      - name: VMT
        dtype: int64
      - name: DRT/ME
        dtype: int64
      - name: Fluid (IRF)
        dtype: int64
      - name: Fluid (SRF)
        dtype: int64
      - name: Disruption of RPE
        dtype: int64
      - name: PED (serous)
        dtype: int64
      - name: SHRM
        dtype: int64
      - name: Eye_ID
        dtype: int64
      - name: BCVA
        dtype: int64
      - name: CST
        dtype: int64
      - name: Patient_ID
        dtype: int64
      - name: Disease Label
        dtype: float64
    splits:
      - name: train
        num_bytes: 1741550062.108
        num_examples: 6958
      - name: test
        num_bytes: 613225346.2
        num_examples: 2450
    download_size: 4022660686
    dataset_size: 2354775408.308
configs:
  - config_name: biomarker_detection
    data_files:
      - split: train
        path: biomarker_detection/train-*
      - split: test
        path: biomarker_detection/test-*
  - config_name: disease_classification
    data_files:
      - split: train
        path: disease_classification/train-*
      - split: test
        path: disease_classification/test-*

OLIVES_Dataset

Abstract

Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: ($i$) they view the problem as disease prediction without assessing biomarkers, and ($ii$) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with $49$ OCT scans, and $16$ biomarkers, along with $3$ clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.

Subsets

There are 3 subsets included in this dataset:

Disease Classification (disease_classification)

Disease Classification

This subset contains information regarding 78,000+ OCT scans obtained from a series of visits patients performed. In terms of labels, there are:

Image: An image of the OCT scan
BCVA: Best Central Visual Acuity
CST: Central Subfield Thickness
Patient ID: A value to help distinguish different patients
Disease Label: A value of 0 for DR (Diabetic Retinopathy) and 1 for DME (Diabetic Macular Edema)

This information can be used to classify the disease. In addition, the first and last visit of a patient included extra biomarker information. This can be summarized into these 16 mostly-boolean labels:

Scan (n/49): The scan number out of the 49 scans taken for each patient
Atrophy / thinning of retinal layer
Disruption of EZ: Disruption of Ellipsoid Zone
DRIL: Disruption of Retinal Inner Layers
IR hemorrhages: Intraretinal hemorrhages
IR HRF: Intraretinal Hyperreflective Foci
Partially attached vitreous face
Fully attached vitreous face
Preretinal tissue/hemorrhage
Vitreous debris
VMT: Vitreomacular Traction
DRT/ME: Diffuse Retinal Thickening or Macular Edema
Fluid (IRF): Intraretinal Fluid
Fluid (SRF): Subretinal Fluid
Disruption of RPE: Disruption of Retinal Pigment Epithelium
PED (serous): Pigment Epithelial Detachment
SHRM: Subretinal Hyperreflective Material
Eye_ID: A value to help distinguish different eye scans

Data Download

Sample code to download the disease classification dataset:

from datasets import load_dataset
from torch.utils.data import DataLoader

olives = load_dataset('gOLIVES/OLIVES_Dataset', 'disease_classification', split = 'train')

# Covert into a Format Usable by Pytorch
olives = olives.with_format("torch")

dataloader = DataLoader(olives, batch_size=4)
for batch in dataloader:
    print(batch) 

# Example to get the VMT Biomarker of the first image in the dataset.
print(olives[0]['VMT'])

Known Issues

Patient ID #79 has missing BCVA and CST for most visits except the first and last visit as the biomarker information is present
Certain visits for patients seem to have the exact same scans as a previous visit. For instance Patient ID #61 has identical images in W8 and their next visit in W12.

Links

Associated Website: https://alregib.ece.gatech.edu/

Citations

If you find the work useful, please include the following citation in your work:

@inproceedings{prabhushankarolives2022,
title={OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics},
author={Prabhushankar, Mohit and Kokilepersaud, Kiran and Logan, Yash-yee and Trejo Corona, Stephanie and AlRegib, Ghassan and Wykoff, Charles},
booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2 (NeurIPS Datasets and Benchmarks 2022)},
year={2022}
}