This article introduces 3D segmentation, partitioning volumetric data into labeled regions for applications in medical imaging, robotics, and more. Focusing on 3D semantic segmentation, it uses the Swin UNETR architecture for brain tumor segmentation as an example. The article covers core concepts, training on the BraTS dataset including MRI normalization, input/output processing, computational challenges, and adapting Swin UNETR for 3D image classification.

The Segment Anything Model 2 (SAM-2) transformed video object segmentation with its memory-based architecture for sequential frames. However, it struggles with occlusions and error propagation. Derivative models like SAMURAI and SAM2Long address these issues by integrating advanced memory and motion-aware mechanisms, improving segmentation accuracy and long-term tracking

This article discusses the challenges of image segmentation and compares dense and sparse bitmask formats. It introduces Run-Length Encoding (RLE) and Run-End Encoding (REE) as efficient solutions for storing segmentation masks. REE improves space efficiency and speed by enabling faster pixel lookup and Boolean operations. Binary tree compression is explored to further optimize REE for large-scale tasks.