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Abstract #4302

Improving Substantia Nigra Segmentation Across Different Neuromelanin-Sensitive MRI Sequences Using Domain Generalization Techniques

Oliver Gabriel Bransby Welsh1,2, Kilian Hett1, Anna Bosman2, Daniel Claassen1, and Paula Trujillo1
1Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, 2Department of Computer Science, University of Pretoria, Pretoria, South Africa

Synopsis

Keywords: Segmentation, Multimodal, Domain Generalization, Substantia Nigra

Motivation: Automatic segmentation of the substantia nigra (SN) in neuromelanin-sensitive MRI (NM-MRI) is challenging due to variations across NM-MRI sequences.

Goal(s): To enhance SN segmentation using domain generalization techniques, creating a domain-agnostic segmentation model robust to variation in sequence parameters.

Approach: A U-Net model was trained on an individual sequence and tested on unseen ones. Data augmentation techniques were applied to address structural and intensity variations across fundamental sequence differences.

Results: Preliminary results suggest that while data augmentation can improve SN segmentation across different sequences, robust deep-learning-based segmentation remains challenging with sequence effects hindering the model's ability to automatically delineate SN across unseen parameters.

Impact: Applying data augmentation techniques significantly enhances automated substantia nigra segmentation in neuromelanin-sensitive MRI, advancing the development of robust, clinically reliable models adaptable to various imaging methods and neurodegenerative conditions.

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