“We were very happy with the quality of the data that we purchased. It will assist our efforts to improve our product. The team at Unidata was very responsive to our needs, and provided the data format needed to complete our tasks. We are happy to state that Unidata delivers a high quality product in an effective and efficient manner.”
Commercial
OCT Retinal Dataset
OCT dataset features 1,000 volumetric OCT scans (B-scans) of the retina with annotated layers, fluid regions, and clinical diagnoses, including AMD, diabetic retinopathy, and glaucoma. This DiCOM dataset supports AI model training in disease classification, pathology segmentation, and computer vision–based retinal analysis using coherence tomography images.
-
- Scans
- 1,000
-
- B-scans per volume
- 100
- Medicine
- Computer vision
- Machine Learning
- Segmentation
- Classification
- Medicine
- Computer vision
- Machine Learning
- Segmentation
- Classification
OCT dataset features 1,000 volumetric OCT scans (B-scans) of the retina with annotated layers, fluid regions, and clinical diagnoses, including AMD, diabetic retinopathy, and glaucoma. This DiCOM dataset supports AI model training in disease classification, pathology segmentation, and computer vision–based retinal analysis using coherence tomography images.
- Medicine
- Computer vision
- Machine Learning
- Segmentation
- Classification
-
- Scans
- 1,000
-
- B-scans per volume
- 100
Dataset Info
| Characteristic | Data |
| Description | Volumetric OCT scans (B-scans) of the retina with annotated layers, fluid regions, and clinical diagnoses |
| Data types | DiCOM |
| Annotation | Retinal layer boundaries, fluid regions, diagnosis labels (normal/AMD/diabetic/glaucoma). |
| Tasks | Recognition, classification and segmentation of pathologies, computer vision |
| Number of files | 1,000 |
| Labeling | Metadata(age, gender, device model, scan position) |
Technical
Characteristics
| Characteristic | Data |
| File Extension | DiCOM |
| File Resolution | Min = 512×512 px |
| Labeling Format | Json |
Dataset Use Cases
FAQs
What is OCT Retinal Dataset used for?
This dataset is designed for training and evaluating machine learning models in retinal disease classification, automated segmentation, and computer vision tasks. It supports applications such as OCT layer segmentation, pathology detection, and medical image analysis for diagnosis automation.
What types of annotations are provided in this retinal dataset?
Annotations include retinal layer boundaries, fluid regions, and diagnosis labels (normal, AMD, diabetic retinopathy, and glaucoma). Each OCT image is accompanied by structured JSON metadata, supporting precise segmentation and disease classification tasks.
Can I request a sample of the retinal OCT dataset before purchasing it?
Yes. Free sample data is available for preview, allowing researchers to test data quality, annotation structure, and compatibility with their models before purchasing the dataset.
How are Unidata datasets stored and managed?
Unidata securely stores all datasets on AWS cloud infrastructure, aligned with ISO 27001 and ISO 27701 standards. This ensures data availability, reliability, and strong safeguards for sensitive or medical imaging data.
How long does it take to receive the dataset after purchase?
After submitting a request, our team will contact you to confirm details and complete the documentation process. Following payment and agreement, OCT Retinal Dataset will be delivered securely within 3–10 business days.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for trial and testing, while full datasets are accessible exclusively through purchase. Licensing terms ensure lawful academic, research, and commercial usage.
Is this a real-world dataset or synthetic data?
OCT Retinal Dataset comprises real-world medical imaging data derived from actual retinal scans. Each image captures authentic anatomical variations, ensuring realistic training data for neural networks and medical AI research.
How can an OCT retinal dataset support AI-based eye disease detection?
An OCT retinal dataset helps AI models learn patterns associated with retinal structures and eye conditions. It supports the development of medical imaging solutions for automated disease classification, pathology detection, and clinical decision support research.
Still have questions about using Unidata datasets?
Read our user-guides
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