rishuKumar404/MedUI_3DSlicer_CSV

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:

{
  "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

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

@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