Abstract
This study presents a preliminary analysis of three image segmentation algorithms for brain tumor detection in Magnetic Resonance Imaging (MRI): Segment Anything Model (SAM), SAM for medical imaging, and a novel algorithm called "Brain Killer" (BK). The research utilized a public open-source dataset, focusing on meningioma, glioma, and pituitary tumors. SAM, a transformer-based model pre-trained on a massive dataset, showed high-quality results on instance segmentation task across all tumor types. SAM for medical imaging, optimized for DICOM files, showed improved precision in tumor boundary detection. Our algorithm, BK, a novel unsupervised algorithm based on patch-based k-Means clustering, provided detailed segmentation including internal tumor characteristics. The results underscore the complementary strengths of supervised and unsupervised approaches in medical image analysis, suggesting potential for integrated solutions in clinical applications.