• Advanced the SAM and MedSAM models' capabilities in 3D MRI bone segmentation using PyTorch, achieving notable diagnostic precision.• Processed extensive MRI datasets into 10,000 2D slices for robust model training and validation.• Enhanced segmentation accuracy through innovative techniques such as data augmentation, prompt mask decoding, and integrating YOLOv7 object detection.• Achieved IoU and DSC performance metrics exceeding 0.8, demonstrating exceptional model reliability on test datasets.

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