ENH: Add Heart-Simpleware-Segmentation experiment and simpleware_medical module

.gitignore

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 # Project files
 .claude
 .coverage
+*.code-workspace
 coverage.xml
 *~
 *.swp

experiments/Heart-Simpleware_Segmentation/.gitignore

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+# Ignore results directory and temporary files
+results/
+*.pyc
+__pycache__/
+.ipynb_checkpoints/
+*.tmp
+*.log
+
+# Ignore large data files
+*.nii
+*.nii.gz
+*.mha
+*.mhd
+*.raw
+*.dcm
+
+# Ignore visualization outputs
+*.png
+*.jpg
+*.jpeg
+*.mp4
+*.avi

experiments/Heart-Simpleware_Segmentation/README.md

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+# Heart Segmentation using Simpleware Medical
+
+This experiment demonstrates cardiac segmentation using Synopsys Simpleware Medical's ASCardio module integrated with PhysioMotion4D.
+
+## Overview
+
+The `SegmentHeartSimpleware` class provides integration between PhysioMotion4D and Synopsys Simpleware Medical for automated heart segmentation. This experiment shows how to:
+
+1. Load cardiac CT images
+2. Segment heart structures using ASCardio
+3. Visualize and analyze the results
+4. Export data for further processing
+
+## Requirements
+
+### Software Requirements
+
+- **Synopsys Simpleware Medical X-2025.06 or later**
+  - Default installation path: `C:\Program Files\Synopsys\Simpleware Medical\X-2025.06\`
+  - Console mode: `ConsoleSimplewareMedical.exe` (command-line version)
+  - ASCardio module license required
+
+- **Python packages** (included in PhysioMotion4D environment):
+  - `itk`
+  - `numpy`
+  - `matplotlib`
+  - `pyvista` (optional, for 3D visualization)
+
+### Data Requirements
+
+- Cardiac CT image (3D volume)
+- Recommended: Gated cardiac CT or high-resolution heart scan
+- Format: NIfTI (.nii, .nii.gz) or any ITK-readable format
+- Image should include complete heart anatomy
+
+## Files
+
+- **`simpleware_heart_segmentation.ipynb`**: Main demonstration notebook
+- **`README.md`**: This file
+- **`results/`**: Output directory (created automatically)
+
+## Usage
+
+### Quick Start
+
+1. Open `simpleware_heart_segmentation.ipynb` in Jupyter
+2. Update the `input_image_path` in cell 3 to point to your cardiac CT image
+3. Run all cells sequentially
+
+### Configuration
+
+Before running, configure these parameters in the notebook:
+
+```python
+# Set input image path
+input_image_path = "/path/to/your/cardiac_ct.nii.gz"
+
+# Set custom Simpleware path (if not default)
+custom_simpleware_path = "D:/CustomPath/Simpleware/ConsoleSimplewareMedical.exe"
+```
+
+### Expected Output
+
+The notebook generates:
+
+1. **Segmentation files** (in `results/`):
+   - `heart_labelmap_simpleware.nii.gz` - Complete labelmap with all structures
+   - `heart_mask_simpleware.nii.gz` - Binary mask of heart structures
+   - `vessels_mask_simpleware.nii.gz` - Binary mask of major vessels
+   - `contrast_mask_simpleware.nii.gz` - Contrast-enhanced regions
+
+2. **Visualizations**:
+   - 2D slice views with segmentation overlays
+   - 3D surface renderings (if PyVista available)
+   - Statistical analysis tables
+
+## Segmented Structures
+
+### Heart Structures (Label IDs 1-6)
+
+- **1**: Left Ventricle (LV)
+- **2**: Right Ventricle (RV)
+- **3**: Left Atrium (LA)
+- **4**: Right Atrium (RA)
+- **5**: Myocardium
+- **6**: Heart (combined heart mask)
+
+### Major Vessels (Label IDs 7-10)
+
+- **7**: Aorta
+- **8**: Pulmonary Artery
+- **9**: Right Coronary Artery
+- **10**: Left Coronary Artery
+
+## Workflow Details
+
+### Step-by-Step Process
+
+1. **Preprocessing** (automatic):
+   - Resampling to 1.0mm isotropic spacing
+   - Intensity normalization if needed
+
+2. **Simpleware Integration**:
+   - Input image saved to a temporary NIfTI file
+   - ConsoleSimplewareMedical.exe is launched with `--input-file` (NIfTI) and `--input-value` (output directory)
+   - The Simpleware script runs ASCardio on the loaded image and exports per-structure masks as MHD
+   - PhysioMotion4D assembles the labelmap from the mask files and returns results
+
+3. **Postprocessing** (automatic):
+   - Labelmap resampled to original image space
+   - Anatomical masks created (heart, vessels, etc.)
+   - Optional contrast agent detection
+
+4. **Analysis & Visualization**:
+   - Volume calculations for each structure
+   - 2D slice visualization
+   - 3D surface rendering
+
+### Processing Time
+
+Typical processing times:
+- Small CT (256³ voxels): 2-5 minutes
+- Medium CT (512³ voxels): 5-10 minutes
+- Large CT (1024³ voxels): 10-20 minutes
+
+Times depend on image size, system performance, and Simpleware configuration.
+
+## Troubleshooting
+
+### Common Issues
+
+**Issue**: `FileNotFoundError: Simpleware Medical executable not found`
+
+**Solution**:
+- Verify Simpleware installation
+- Ensure you're using `ConsoleSimplewareMedical.exe` (not `SimplewareMedical.exe`)
+- Update `custom_simpleware_path` in the notebook
+- Check default path: `C:\Program Files\Synopsys\Simpleware Medical\X-2025.06\ConsoleSimplewareMedical.exe`; use `set_simpleware_executable_path()` if installed elsewhere
+
+---
+
+**Issue**: `unrecognised option '-python'` or similar command-line errors
+
+**Solution**:
+- Use `ConsoleSimplewareMedical.exe` (console version), not `SimplewareMedical.exe` (GUI version)
+- The script is automatically called with `--run-script` flag
+
+---
+
+**Issue**: `ImportError: Failed to import Simpleware modules`
+
+**Solution**:
+- Ensure ASCardio module is licensed
+- Verify Simpleware version is X-2025.06 or later
+- Check that console mode is properly installed
+
+---
+
+**Issue**: `WARNING: No segmentation masks were created` or missing masks
+
+**Solution**:
+- Ensure the input NIfTI is passed correctly via `--input-file` so Simpleware has an active document
+- Check that ASCardio completed successfully (inspect Simpleware stdout in debug logging)
+- Verify input image contains clear heart anatomy and adequate contrast
+
+---
+
+**Issue**: Input image quality issues
+
+**Solution**:
+- Verify input image contains heart anatomy
+- Check image contrast and quality
+- Ensure proper field of view (complete heart visible)
+- Try adjusting image preprocessing parameters
+
+---
+
+**Issue**: Segmentation timeout after 600 seconds
+
+**Solution**:
+- Image may be too large; consider downsampling
+- Check system resources (CPU, RAM)
+- Increase timeout in `segment_heart_simpleware.py` if needed
+
+---
+
+**Issue**: Poor segmentation quality
+
+**Solution**:
+- Ensure image has adequate contrast
+- Use contrast-enhanced CT if available
+- Check that heart is centered in field of view
+- Verify image orientation is correct
+
+### Debug Mode
+
+Enable detailed logging:
+
+```python
+import logging
+segmenter = SegmentHeartSimpleware(log_level=logging.DEBUG)
+```
+
+This provides:
+- Detailed subprocess output
+- Simpleware script messages
+- File I/O operations
+- Timing information
+
+## Integration with Other Workflows
+
+This experiment can be combined with other PhysioMotion4D workflows:
+
+### 4D Heart Animation
+Use segmentation results with `Heart-GatedCT_To_USD` workflow:
+```python
+# After segmentation
+from physiomotion4d.workflow_convert_heart_gated_ct_to_usd import WorkflowConvertHeartGatedCTToUSD
+
+workflow = WorkflowConvertHeartGatedCTToUSD()
+workflow.set_static_labelmap(result["labelmap"])
+# Continue with 4D USD generation
+```
+
+### Statistical Model Registration
+Register segmentation with heart model using `Heart-Statistical_Model_To_Patient`:
+```python
+from physiomotion4d.workflow_register_heart_model_to_patient import WorkflowRegisterHeartModelToPatient
+
+workflow = WorkflowRegisterHeartModelToPatient()
+workflow.set_patient_segmentation(result["labelmap"])
+# Perform model-to-patient registration
+```
+
+### Custom Analysis
+Extract specific structures for analysis:
+```python
+# Get left ventricle only
+lv_mask = np.where(labelmap_array == 1, 1, 0)
+
+# Calculate LV volume
+lv_volume_ml = np.sum(lv_mask) * voxel_volume / 1000
+
+# Create mesh for computational modeling
+from physiomotion4d.convert_vtk_to_usd import create_mesh_from_mask
+lv_mesh = create_mesh_from_mask(lv_mask)
+```
+
+## Performance Optimization
+
+### For Faster Processing
+
+1. **Reduce image resolution**:
+```python
+# Before segmentation
+segmenter.set_target_spacing(2.0)  # Use 2mm instead of 1mm
+```
+
+2. **Use region of interest**:
+```python
+# Crop image to heart region before segmentation
+from physiomotion4d.image_tools import crop_to_roi
+cropped_image = crop_to_roi(input_image, roi_bounds)
+```
+
+## References
+
+- **Simpleware Medical Documentation**: See Synopsys user manual
+- **ASCardio Module**: Refer to ASCardio technical documentation
+- **PhysioMotion4D**: Main repository documentation
+
+## Citation
+
+If using this integration in research, please cite:
+- Synopsys Simpleware Medical
+- PhysioMotion4D framework
+- Any relevant papers using ASCardio segmentation
+
+## License
+
+This integration code follows the PhysioMotion4D license. Simpleware Medical and ASCardio are commercial products requiring separate licenses from Synopsys.
+
+## Support
+
+For issues with:
+- **PhysioMotion4D integration**: Submit issue to PhysioMotion4D repository
+- **Simpleware Medical/ASCardio**: Contact Synopsys support
+- **This experiment**: Check troubleshooting section above
+
+## Version History
+
+- **v0.2.0** (2026-02-06): Documentation and alignment with current implementation
+  - README reflects actual notebook name (`simpleware_heart_segmentation.ipynb`) and correct label IDs (heart 1–6, vessels 7–10)
+  - Workflow description updated for `--input-file` (NIfTI) and `--input-value` (output dir) invocation
+  - Removed obsolete “placeholder output” limitation; integration works as expected
+- **v0.1.0** (2026-02-04): Initial implementation
+  - Basic ASCardio integration
+  - Heart and vessel segmentation
+  - 2D/3D visualization