README.md

---
license: mit
tags:
- medical-imaging
- image-segmentation
- white-matter-hyperintensities
- mri
- flair
- deep-learning
- tensorflow
- keras
- neurology
- multiple-sclerosis
datasets:
- custom
- msseg2016
metrics:
- dice-coefficient
- hausdorff-distance
library_name: tensorflow
pipeline_tag: image-segmentation
---

# WMH Segmentation: Normal vs Abnormal Classification

Pre-trained models for **white matter hyperintensity (WMH) segmentation** with explicit distinction between normal periventricular changes and pathological lesions.

## Model Description

This repository contains 8 pre-trained deep learning models (4 architectures × 2 training scenarios) for automated WMH segmentation from FLAIR MRI images. The models implement a novel **three-class approach** that distinguishes between:

- **Class 0**: Background
- **Class 1**: Normal WMH (aging-related periventricular changes)
- **Class 2**: Abnormal WMH (pathologically significant lesions)

This approach addresses the critical challenge of false positive detection in periventricular regions, achieving up to **27.1% improvement** in Dice coefficient compared to traditional binary segmentation.

## Model Architectures

| Architecture | Parameters | Best Dice (3-Class) | Binary Baseline | Improvement |
|--------------|-----------|---------------------|-----------------|-------------|
| **U-Net** ⭐ | 31.0M | **0.768** | 0.497 | **+54.5%** |
| **Attention U-Net** | 34.9M | 0.740 | 0.486 | +52.1% |
| **TransUNet** | 105.3M | 0.700 | 0.510 | +37.3% |
| **DeepLabV3Plus** | 40.3M | 0.586 | 0.374 | +56.7% |

⭐ **Recommended**: U-Net with Scenario 2 (three-class) for optimal performance

## Repository Structure

```
models/
├── unet/models/
│   ├── scenario1_binary_model.h5          # Binary: Background vs Abnormal
│   └── scenario2_multiclass_model.h5      # 3-Class: Background, Normal, Abnormal
├── attention_unet/models/
│   ├── scenario1_binary_model.h5
│   └── scenario2_multiclass_model.h5
├── deeplabv3plus/models/
│   ├── scenario1_binary_model.h5
│   └── scenario2_multiclass_model.h5
└── transunet/models/
    ├── scenario1_binary_model.h5
    └── scenario2_multiclass_model.h5
```

## Quick Start

### Installation

```bash
pip install huggingface_hub tensorflow numpy nibabel
```

### Download Models

```python
from huggingface_hub import hf_hub_download

# Download best performing model (U-Net Three-Class)
model_path = hf_hub_download(
    repo_id="Bawil/wmh_leverage_normal_abnormal_segmentation",
    filename="unet/models/scenario2_multiclass_model.h5"
)

# Load model
from tensorflow.keras.models import load_model
model = load_model(model_path)
```

### Inference Example

```python
import numpy as np
from tensorflow.keras.models import load_model

# Load pre-trained model
model = load_model(model_path)

# Prepare input (256x256 grayscale FLAIR MRI, normalized)
# input_image shape: (batch_size, 256, 256, 1)
input_image = preprocess_flair(your_flair_image)

# Run inference
predictions = model.predict(input_image)

# Get class predictions
predicted_classes = np.argmax(predictions, axis=-1)
# 0: Background
# 1: Normal WMH (periventricular)
# 2: Abnormal WMH (pathological)

# Extract pathological lesions only
abnormal_mask = (predicted_classes == 2).astype(np.uint8)
```

## Training Data

### Dataset Composition

- **Local Dataset**: 100 MS patients (2,000 FLAIR MRI slices)
  - Demographics: 26 males, 74 females
  - Age range: 18-68 years
  - Scanner: 1.5-Tesla TOSHIBA Vantage
  
- **Public Dataset**: MSSEG2016 (15 patients, 750 FLAIR slices)

### Annotations

- Expert annotations by board-certified neuroradiologists (20+ years experience)
- Three-class labeling: Background, Normal WMH, Abnormal WMH
- Approved by Ethics Committee (IR.TBZMED.REC.1402.902)

### Data Split

- **Training**: 80% patients (local) + 60% patients (public)
- **Validation**: 10% patients (local) + 20% patients (public)
- **Testing**: 10% patients (local) + 20% patients (public)
- **Strategy**: Patient-level stratified split (no slice-level leakage)

## Model Training

### Configuration

- **Framework**: TensorFlow 2.11, Keras
- **Optimizer**: Adam (learning rate: 0.0001)
- **Loss Functions**:
  - Scenario 1: Weighted binary cross-entropy
  - Scenario 2: Weighted categorical cross-entropy
- **Epochs**: 50 (with early stopping)
- **Batch Size**: 8
- **Input Size**: 256×256×1
- **Data Augmentation**: Rotation, flipping, elastic deformation

### Hardware

- **GPU**: NVIDIA RTX 3060 (12GB VRAM)
- **Training Time**: 2-3 hours per model
- **Inference Time**: ~35-40ms per image

## Model Performance

### Dice Coefficient (Primary Metric)

| Model | Scenario 1 | Scenario 2 | Δ Improvement | p-value | Cohen's d |
|-------|-----------|-----------|---------------|---------|-----------|
| U-Net | 0.497±0.145 | **0.768±0.124** | **+0.271** | <0.0001 | 0.564 |
| Attention U-Net | 0.486±0.157 | 0.740±0.133 | +0.253 | <0.0001 | 0.442 |
| TransUNet | 0.510±0.116 | 0.700±0.097 | +0.190 | <0.0001 | 0.478 |
| DeepLabV3Plus | 0.374±0.110 | 0.586±0.092 | +0.212 | <0.0001 | 0.565 |

### Additional Metrics

- **Hausdorff Distance**: 27.4mm (U-Net 3-class) vs 29.8mm (binary)
- **Precision**: Significant improvement in pathological lesion detection
- **False Positive Reduction**: Marked decrease in periventricular regions
- **Clinical Feasibility**: 1.5s total processing time per case (40 slices)

### Statistical Validation

- Paired t-tests confirm significant improvements (all p < 0.0001)
- Effect sizes range from medium (0.44) to large (0.56)
- 95% confidence intervals reported for all metrics
- Wilcoxon signed-rank test for non-parametric validation

## Use Cases

### Clinical Applications

- **MS Lesion Quantification**: Accurate measurement of disease burden
- **Differential Diagnosis**: Distinguish pathological from normal aging
- **Longitudinal Monitoring**: Track disease progression over time
- **Treatment Response**: Evaluate therapeutic efficacy
- **Radiological Reporting**: Reduce false positive alerts

### Research Applications

- **Baseline Comparisons**: Standardized evaluation framework
- **Method Development**: Foundation for advanced segmentation approaches
- **Multi-center Studies**: Protocol for broader validation
- **Reproducible Research**: Complete implementation available

## Limitations

- **Single Modality**: Trained on FLAIR MRI only
- **Scanner Specificity**: Primarily 1.5T TOSHIBA data
- **Disease Focus**: Optimized for MS patients
- **2D Segmentation**: Slice-by-slice processing (no 3D context)
- **Resolution**: Fixed 256×256 input size

## Model Card

### Intended Use

- **Primary**: Automated WMH segmentation for research and clinical decision support
- **Users**: Radiologists, neurologists, researchers, AI developers
- **Out-of-scope**: Not FDA/CE approved; not for standalone clinical diagnosis

### Ethical Considerations

- **Privacy**: All data anonymized per HIPAA/GDPR standards
- **Bias**: Limited scanner/protocol diversity may affect generalization
- **Clinical Validation**: Requires expert review before clinical use
- **Transparency**: Complete methodology and code openly available

### Model Card Authors

Mahdi Bashiri Bawil, Mousa Shamsi, Ali Fahmi Jafargholkhanloo, Abolhassan Shakeri Bavil

## Citation

```bibtex
@article{bawil2025wmh,
  title={Incorporating Normal Periventricular Changes for Enhanced Pathological 
         White Matter Hyperintensity Segmentation: On Multi-Class Deep Learning Approaches},
  author={Bawil, Mahdi Bashiri and Shamsi, Mousa and Jafargholkhanloo, Ali Fahmi and 
          Bavil, Abolhassan Shakeri},
  year={2025},
  note={Models: https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation}
}
```

## License

MIT License - See [LICENSE](https://github.com/Mahdi-Bashiri/wmh-normal-abnormal-segmentation/blob/main/LICENSE)

## Additional Resources

- **📄 Paper**: [Under Review]
- **💻 GitHub Repository**: [Mahdi-Bashiri/wmh-normal-abnormal-segmentation](https://github.com/Mahdi-Bashiri/wmh-normal-abnormal-segmentation)
- **📧 Contact**: m[email protected]
- **🏥 Institution**: Sahand University of Technology & Tabriz University of Medical Sciences

## Acknowledgments

- **Golgasht Medical Imaging Center**, Tabriz, Iran for providing clinical data
- Expert neuroradiologists for manual annotations
- Ethics Committee approval: IR.TBZMED.REC.1402.902

---

**Keywords**: white matter hyperintensities, FLAIR MRI, medical imaging, deep learning, image segmentation, multiple sclerosis, U-Net, attention mechanisms, transformers, clinical AI