The recent advancement in AI has led to the development of innovative segmentation models, which allow precise object recognition and mask generation from various inputs. The Segment Anything Model (SAM), a new AI segmentation model, is capable of producing high-quality object masks from input prompts for all objects in an image. This research aims to develop pipelines for applying the SAM 2D segmentation model on 3D imaging medical data, enabling the creation of masks for 3D data. This technique will prove particularly useful for tasks such as illustrating the structure of objects in 3D imaging data and locating tremors, which are critical for accurate diagnosis and further analytics.