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Abstract #3171

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

Leen Hakki1, Melisa Özakçakaya1, Belal Tavashi1, Uluç Pamuk2, Oğuzhan Hüraydın2, Esin Öztürk Işık1, and Pınar Senay Özbay1
1Biomedical Engineering, Boğaziçi University, İstanbul, Turkey, 2Bogazici University Center for Targeted Therapy Technologies, Boğaziçi University, Istanbul, Turkey

Synopsis

Keywords: Preclinical Image Analysis, preclinical image analysis, small animal, rat, brain extraction, segmentation

Motivation: Brain extraction is an important preprocessing step in preclinical MRI studies. However, there is a lack of reliable tools that perform an accurate brain extraction for small animals, like rats.

Goal(s): This study aims to develop a deep-learning model for automated rat brain extraction in multi-contrast MRI scans.

Approach: We developed a U-Net model with VGG19 as the encoder. The training was conducted only on T2-weighted images; testing included T2, T1-weighted, and MGE contrasts.

Results: The model achieved a dice coefficient of 0.972 on T2 weighted scans. The model also demonstrated high accuracy across different MRI contrasts.

Impact: The U-Net model resulted in high segmentation accuracy for various rat brain MRI contrasts. This approach reduced the need for manual brain segmentation for a more automated data processing.

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