SAM Cell Segmentation

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Streamline biological image analysis with expert guidance on implementing the Segment Anything Model (SAM) and MobileSAM for complex cell segmentation tasks. This skill provides a structured methodology for processing microscopy images, covering every stage from initial coordinate parsing and deep learning inference to mask refinement and polygon extraction. It emphasizes robust data transformation patterns, such as converting binary masks to valid polygon strings, handling overlapping regions, and ensuring output compliance for scientific research and production data pipelines.

Características Principales

• SAM and MobileSAM model integration and inference patterns
• Binary mask to polygon coordinate conversion techniques
• CSV-based input parsing and data transformation strategies
• Mock testing strategies for deep learning environment constraints
• Overlap removal and mask refinement post-processing
• 16 GitHub stars

Casos de Uso

• Refining coarse segmentation masks into precise polygon coordinates
• Integrating deep learning segmentation models into automated processing scripts
• Automating cell boundary extraction in microscopy imaging