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Abstract #0259

Evaluating a Deep Learning Foundation Model for Neuroimaging Segmentation in the Data-Rich and Data-Constrained Settings

Karthik Nair1, Yvonne W Lui1, and Narges Razavian1
1NYU Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Diagnosis/Prediction, Segmentation

Motivation: Deep learning models for segmentation require large datasets, limiting their use in clinical settings. Foundation models, which learn from non-medical images before fine-tuning for specific tasks, have emerged as a possible solution.

Goal(s): We aimed to adapt and evaluate a recent foundation model, the Medical Segment Anything model (MedSAM), for neuroanatomy segmentation.

Approach: Using the Human Connectome Project dataset, we trained MedSAM to segment 102 regions-of-interest and compared its accuracy with a baseline UNet model.

Results: UNet outperformed MedSAM in almost all regions and dataset sizes, but MedSAM showed potential when training with very few MRIs.

Impact: While foundation models such as MedSAM have potential for medical segmentation, they currently may not surpass traditional models when using sufficient data. In the data-limited setting, however, they can be useful when extremely little labeled data is available.

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