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September 17, 2025Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

Automated Brain Extraction for Multi-Contrast MRI in Rat Models Using Enhanced U-Net

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Authors

LHLeen HakkıYıldız Technical UniversityMÖMelisa ÖzakçakayaBTBelal TavashiAnkara University

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Implication

Deep-learning model improves segmentation accuracy for multi-contrast MRI in rat models, indicating reduced manual effort.

Key Points

  • The model achieved a dice coefficient of 0.972 on T2-weighted scans, indicating high accuracy.
  • High segmentation accuracy across different MRI contrasts was observed, ensuring reliable outcomes.
  • A U-Net model utilizing VGG19 as the encoder was developed, incorporating various MRI contrasts for testing.
  • The findings emphasize the potential of automated brain extraction in enhancing preclinical MRI data processing efficiency.

Cite This Study

Hakkı et al. (2025) studied this question.

synapsesocial.com/papers/68d45b0b31b076d99fa5d1b2https://doi.org/10.58530/2025/3171
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