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Related Experiment Video

Updated: May 31, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A multi-sequence MRI integration framework using SwinUNETR-v2 for multiple sclerosis lesion segmentation.

Rezq Muhammed Thabet1, Howida A Shedeed2, Maryam Al-Berry2

  • 1Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. rizk.mohamed@cis.asu.edu.eg.

BMC Medical Informatics and Decision Making
|May 28, 2026
PubMed
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This summary is machine-generated.

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This study introduces an AI system using multi-sequence MRI scans to accurately detect Multiple Sclerosis (MS) lesions. The advanced SwinUNETR-v2 model significantly improves lesion segmentation, aiding in faster and more reliable MS diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Multiple Sclerosis (MS) is a chronic neurological disease impacting the brain and spinal cord.
  • Magnetic Resonance Imaging (MRI) is crucial for MS diagnosis, but manual analysis is labor-intensive and error-prone.
  • AI-driven Computer Aided Diagnostic (CAD) systems offer consistent and reliable assessments for MS detection.

Purpose of the Study:

  • To develop and evaluate an AI framework for accurate Multiple Sclerosis lesion segmentation using multi-sequence MRI.
  • To enhance the diagnostic process for MS by leveraging advanced AI techniques.

Main Methods:

  • A multi-sequence framework integrating four MRI modalities with the SwinUNETR-v2 backbone was developed.
  • Task-oriented integration included a four-channel volume representation, refined preprocessing with balanced patch extraction, and a weighted loss function.
Keywords:
CAD toolsDeep LearningMRI ModalitiesMultiple Sclerosis

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  • A five-fold cross-validation protocol was employed for rigorous evaluation.

    • Main Results:

    • The proposed framework achieved a peak Dice Similarity Coefficient (DSC) of 90.7% and a mean DSC of 88.3% for MS lesion segmentation.
    • The multi-sequence SwinUNETR-v2 model consistently outperformed other state-of-the-art segmentation methods (AttentionUNet, DenseResidualUNet, SegResNet, FCNN, nnUNet-v2).

    Conclusions:

    • The AI-driven multi-sequence MRI framework demonstrates strong effectiveness in identifying Multiple Sclerosis lesions.
    • This approach offers a more consistent, reliable, and efficient alternative to manual analysis for MS diagnosis.