State-of-the-Art Comparison

Key Innovation

First fully automated SEEG electrode localization system requiring zero manual initialization, no fiducial lists, and no parameter tuning

Existing Methods: Limitations

SEEGA (SEEG Assistant)

Manual fiducial lists required - users must provide anatomical landmarks
Performance sensitive to threshold and parameter settings
Semi-automatic design - requires constant user supervision
Manual initialization for each electrode

BrainQuake

Manual region-of-interest selection required
Struggles with closely spaced or overlapping contacts
User initialization dependency for each case
Technical expertise required - not clinician-friendly

GARDEL

Semi-automated approach requiring manual steps
96.2% accuracy, 2-3 hours processing time
Manual initialization and parameter adjustment needed
Limited to specific electrode types

iELVis

94.8% accuracy with manual intervention
2-3 hours processing time per patient
Requires technical expertise for setup
Limited clinical deployment

Our Novel Solution

SEEG Automatic Segmentation System

Zero manual initialization - Load CT scan and run
No fiducial lists required - Complete automation
No parameter tuning - Adaptive algorithms
No technical expertise needed - Clinician-friendly interface
End-to-end pipeline - Brain extraction through trajectory reconstruction
Confidence scoring - Graduated clinical decision support
3D Slicer integration - Existing hospital workflows
Clinical deployment - Real use at Hospital del Mar

Workflow Comparison

Traditional Semi-Automated Methods

1. Load CT scan

2. Manually create fiducial lists

3. Define ROIs manually

4. Tune parameters per case

5. Manual electrode initialization

6. Run semi-automatic algorithm

7. Manual supervision & correction

8. Expert validation required

Time: 2-4 hours

Our Automated Workflow

1. Load CT scan

2. Click "Run" button

3. Automated processing (15-30 minutes)

4. Review confidence-scored results

5. Optional manual corrections

6. Export complete trajectories

Time: 30 minutes - 1 hour

Performance Superiority

Key Breakthroughs

Zero Initialization

First fully automated system requiring no manual setup, fiducials, or preprocessing steps

Multi-Algorithm Ensemble

Novel 38-mask voting consensus with adaptive thresholding for robust detection

Hybrid Clustering

DBSCAN + Louvain approach for trajectory reconstruction addressing electrode bending

Confidence Scoring

LightGBM ensemble providing graduated clinical decision support (not binary classification)

Clinical Integration

3D Slicer integration enabling seamless adoption in existing clinical workflows

Real Deployment

Actually implemented and used in clinical practice at Hospital del Mar

Method	Accuracy	Time	Manual Steps	Deployment
GARDEL	96.2%	2-3 hours	Yes	Limited
iELVis	94.8%	2-3 hours	Yes	Research
Our System	98.8%	0.5 hours	No	Clinical