File size: 3,799 Bytes
4f9c5fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# 3D Slicer Medical Imaging GUI Benchmark Dataset (CSV Format)
## Dataset Description
This dataset contains **315 end-to-end GUI automation tasks** for 3D Slicer medical imaging software, focusing on MRI brain analysis workflows.
### Dataset Summary
- **Total Tasks**: 315
- **Total Images**: 100 unique screenshots (file paths only)
- **Application**: 3D Slicer (medical imaging software)
- **Domain**: Medical imaging, MRI brain analysis
- **Format**: CSV with file paths (ultra memory-efficient)
### Supported Tasks
- GUI automation
- Medical imaging workflows
- Visual grounding
- Action prediction
- Task planning
## Dataset Structure
The dataset is provided as a CSV file with the following columns:
- `serial_number`: Task number (1-315)
- `task_id`: Unique identifier (e.g., "3dslicer_endtoend_001")
- `task`: Natural language task description
- `image_sequence`: Screenshot sequence (→ separated)
- `json_data`: Complete task data in JSON format
- `num_steps`: Number of steps in the trajectory
- `num_images`: Number of images for this task
- `image_paths`: Pipe-separated file paths to images
- `images_dir`: Base directory for images
### JSON Data Structure
The `json_data` field contains:
```json
{
"id": "3dslicer_endtoend_001",
"initial_state": {
"application": "3D Slicer",
"display_resolution": [1920, 1080],
"loaded_image": "Import_Akash_Data.png"
},
"instruction": "Task description...",
"trajectory": [
{
"step": 1,
"action": "CLICK",
"target": "Load Data (Akash)",
"screenshot": "Import_Akash_Data.png",
"note": "Step 1: Interacting with UI elements",
"bbox": [1054, 0, 1089, 35]
}
],
"outputs": {
"final_file": "task_1_output.png",
"verification": {...},
"success": true
}
}
```
### Action Types
- **CLICK**: Button clicks, menu selections (71.1%)
- **SEGMENT**: Drawing ROIs, measurements (15.9%)
- **COMPLETE**: Task completion (5.8%)
- **TEXT**: Text input (3.2%)
- **ZOOM**: Zoom operations (2.0%)
- **SCROLL**: Navigation (2.0%)
## Usage
```python
import pandas as pd
import json
from PIL import Image
import os
# Load CSV dataset
df = pd.read_csv("3dslicer_benchmark.csv")
# Access a task
task = df.iloc[0]
print(f"Task: {task['task']}")
print(f"Steps: {task['num_steps']}")
# Parse JSON data
task_json = json.loads(task['json_data'])
print(f"Trajectory: {len(task_json['trajectory'])} steps")
# Load images on-demand
image_paths = task['image_paths'].split('|')
for i, img_path in enumerate(image_paths):
if os.path.exists(img_path):
img = Image.open(img_path)
print(f"Image {i+1}: {img.size}")
```
## Memory Efficiency
This CSV-based approach provides:
- ✅ **Ultra-low memory usage** - no images loaded into memory
- ✅ **Fast loading** - CSV loads in seconds
- ✅ **Flexible access** - load images only when needed
- ✅ **Easy sharing** - single CSV file
- ✅ **Scalable** - works with any number of images
## Dataset Creation
This dataset was created using:
- Manual annotation of 3D Slicer workflows
- Automated bounding box extraction (red/orange/yellow highlights)
- Robust action inference with strict guardrails
- Ultra memory-efficient CSV processing
### Quality Assurance
- ✅ 100% consistent actions for same UI elements
- ✅ 100% consistent bounding boxes for same screenshots
- ✅ Only CLICK actions have bounding boxes
- ✅ All bounding boxes extracted from images
- ✅ Strict guardrails prevent inconsistencies
- ✅ Ultra memory-efficient processing
## Citation
```bibtex
@dataset{3dslicer_benchmark_2024,
title={3D Slicer Medical Imaging GUI Benchmark Dataset},
author={Rishu Kumar},
year={2024},
url={https://huggingface.co/datasets/rishuKumar404/MedUI_3DSlicer_CSV}
}
```
## License
MIT License
|