README.md

# 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