Neuro-AI: AI-Driven MS Lesion Analysis Framework

Ventricles & WMH Segmentation:

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

Model Description

This repository should contain 6 pre-trained deep learning models (6 architectures) for automated, simultaneous Ventricles and WMH segmentation from FLAIR MRI images. The models implement a novel four-class approach that distinguishes between:

Class 0: Background
Class 1: Ventricular system
Class 2: Normal WMH (aging-related periventricular changes)
Class 3: Abnormal WMH (pathologically significant lesions)

Model Architectures

Architecture	mean DSC	mean IoU	mean HD95	mean Precision	mean Recall
Baseline U-Net (WCE)	0.714 ±0.018	0.601 ±0.020	6.50 ±0.46	0.616 ±0.020	0.937 ±0.002
Baseline Pix2Pix (WCE)	0.823 ±0.011	0.721 ±0.014	5.31 ±0.20	0.805 ±0.005	0.848 ±0.010
Baseline Pix2Pix (UFL)	0.817 ±0.010	0.714 ±0.012	5.50 ±0.35	0.791 ±0.021	0.850 ±0.019
Pix2Pix + Attention Discriminator	0.824 ±0.008	0.723 ±0.011	5.23 ±0.31	0.807 ±0.014	0.843 ±0.009
Pix2Pix + Adaptive Hybrid Loss	0.844 ±0.002	0.750 ±0.003	4.81 ±0.05	0.845 ±0.009	0.843 ±0.010
Pix2Pix + Attention Discriminator + Adaptive Hybrid Loss ⭐	0.852 ±0.004	0.760 ±0.006	4.87 ±0.13	0.856 ±0.006	0.850 ±0.006

⭐ Recommended: Pix2Pix + Attention Discriminator + Adaptive Hybrid Loss (V5) for best performance

Repository Structure

results_fold_2_var_5_bet_zscore/
└── models/standard_4class/fold_2
    └── best_dice_generator.h5      # 4-Class: Background, Ventricles, Normal, Abnormal

Quick Start

Installation

pip install huggingface_hub tensorflow numpy nibabel

Download Models

from huggingface_hub import hf_hub_download

# Download best performing model (V5)
model_path = hf_hub_download(
    repo_id="Bawil/neuro-ai",
    filename="results_fold_2_var_5_bet_zscore/models/standard_4class/fold_2/best_dice_generator.h5"
)

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

Inference Example

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: Ventricles
# 2: Normal WMH (periventricular)
# 3: Abnormal WMH (pathological)

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

Training Data

Dataset Composition

Local Dataset: 300 MS patients (6,000 FLAIR MRI slices)
- Demographics: 78 males, 222 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)
Four-class labeling: Background, Ventricles, Normal WMH, Abnormal WMH
Approved by Ethics Committee (IR.TBZMED.REC.1402.902)

Data Split

Training: 70% patients (local)
Validation: 10% patients (local)
Testing: 20% patients (local) + 40% patients (public)
Strategy: Patient-level stratified split (no slice-level leakage)

Model Training

Configuration

Framework: TensorFlow 2.11, Keras
Optimizer: Adam (learning rate: 0.0002)
Loss Functions:
- Option 1: Weighted categorical cross-entropy
- Option 2: Unified Focal Dice
Epochs: 60 (with early stopping)
Batch Size: 4
Input Size: 256×256×1

Hardware

GPU: NVIDIA RTX 3060 (12GB VRAM)
Training Time: 3-4 hours per model (5-fold CV)
Inference Time: ~35-40ms per image

Model Performance

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, Abolhassan Shakeri Bavil

Citation

@article{bawil2026neuro,
  title={AI-Driven Multi-Parametric MS Lesion Analysis from T2-FLAIR Imaging: A Clinical Decision Support Framework for Neuroradiology},
  author={Bawil, Mahdi Bashiri and Shamsi, Mousa and Bavil, Abolhassan Shakeri},
  year={2026},
  note={Models: https://huggingface.co/Bawil/neuro-ai}
}

License

MIT License - See LICENSE

Additional Resources

📄 Paper: [Under Review]
💻 GitHub Repository: mri-ms-analyzer/neuro-ai
📧 Contact: m_bashiri99@sut.ac.ir | mehdi.bashiri.b@gmail.com
🏥 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: Artificial intelligence (AI), multiple sclerosis, neuroradiology, clinical decision support, automated lesion analysis, deep learning, clinical AI

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