Stitched Image Quality Control

The Stitched Image Quality Control skill provides a specialized diagnostic and remediation framework for identifying common artifacts in microscopy datasets. It specifically monitors for saturation issues caused by BaSiC flatfield correction failures and visible tile boundaries resulting from improper blending or model mismatches. By providing Python-based diagnostic metrics, root cause analysis for GPU-accelerated processing, and automated reprocessing workflows, it helps researchers and data scientists ensure high-fidelity image outputs for precise downstream scientific analysis.

Key Features

Use Cases