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+# 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