Zero-Shot Skull Stripping for Alzheimer’s Disease Classification With the Segment Anything Model - Pinheiro, Jean Christopher; Pinheiro, Vitória Chrissie de O.; Silva, Rodrigo; Moreira, Gladston; Silva, Pedro H. L.; Luz, Eduardo J. S. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245169, 2024)
This study explores magnetic resonance imaging segmentation for Alzheimer’s disease by automating skull stripping using the Segment Anything Model (SAM), a zero-shot segmentation model. The challenge lies in selecting the correct mask generated by the SAM, for which we propose a heuristic based on templates to identify the optimal choice. This method presents a practical alternative to the traditional FMRIB Software Library Brain Extraction Tool. The effectiveness of our approach is indirectly assessed using Alzheimer’s disease classification as a proxy task. Validation is conducted using the Alzheimer’s Disease Neuroimaging Initiative dataset, demonstrating a 6% improvement in classification accuracy with the zero-shot approach.