metadata
datasets:
- name: Stereotactic Radiosurgery Dataset (SRS)
- description: >-
A comprehensive dataset designed for developing AI models in Stereotactic
Radiosurgery (SRS). This dataset includes clinical, imaging, tumor
segmentation, and treatment planning data to support research in automated
contouring, dose prediction, and treatment optimization.
- license: CC BY-NC 4.0
- tags:
- medical-imaging
- radiotherapy
- tumor-segmentation
- dose-optimization
- AI-healthcare
- languages:
- en
🎯 Stereotactic Radiosurgery Dataset (SRS)
🎯 Overview
The Stereotactic Radiosurgery Dataset is tailored for research in advanced AI applications for Stereotactic Radiosurgery (SRS).
📊 Dataset Summary
| Feature | Details |
|---|---|
| 🏥 Clinical Data | 400 patient records with demographic and medical history information. |
| 🧠 Imaging Data | High-resolution CT, MRI, and PET scans with isotropic and anisotropic voxel sizes. |
| 🎯 Tumor Segmentations | Segmentation paths for GTV, CTV, and PTV with inter-observer variability. |
| ⚙️ Treatment Plans | Beam arrangements, dose distributions, DVHs, and optimization objectives. |
| 📂 File Formats | Metadata in CSV and images/segmentations in NIfTI. |
💡 Features
1. Clinical Data
- Patient demographics: Age, gender, weight, height.
- Medical history: Comorbidities, previous treatments.
- Tumor details: Histology, grade, and stage.
2. Imaging Data
- Modalities: CT, MRI, and PET.
- Imaging protocols: Contrast-enhanced and non-contrast-enhanced scans.
- Scanner metadata: Manufacturer and model.
3. Tumor Segmentation
- Gross Tumor Volume (GTV), Clinical Target Volume (CTV), and Planning Target Volume (PTV).
- Segmentation paths stored in NIfTI format.
4. Treatment Plans
- Dose distributions, beam arrangements, and dose-volume histograms (DVHs).
- Optimization goals: Tumor dose maximization and organ sparing.
🔍 Usage
This dataset supports multiple applications:
- Automated Contouring: Train models for accurate tumor volume delineation.
- Treatment Optimization: Develop algorithms for optimized treatment plans.
- Patient Outcome Prediction: Research predictive analytics for treatment response.
🛠️ File Organization
- Main CSV: Contains metadata for all patient cases.
- Synthetic Images: Paths to synthetic CT, MRI, and PET scans.
- Tumor Segmentations: Paths to NIfTI files for GTV, CTV, and PTV segmentations.
- Simulated Paths: Placeholder paths simulate real-world usage scenarios.
🎨 Visual Example
Below is an example row from the dataset:
| Feature | Example Value |
|---|---|
| Patient_ID | SIM-0001 |
| Age | 36 |
| Imaging Modality | CT |
| Tumor Histology | Meningioma |
| GTV_Segmentation_Path | /simulated/path/SIM-0001_GTV_segmentation.nii |
| Beam Arrangements | Single |
| Dose Distributions (Gy) | 35.04 |
📜 Citations
If you use this dataset, please cite it as follows:
💬 Contact
For questions, reach out to the dataset maintainer: A Taylor @ hf.co/taylor658
🔒 Licensing
This dataset is licensed under apache-2.0.