| --- |
| 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_bashiri99@sut.ac.ir |
| - **🏥 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 |
| |
