cell-detection

Automating cell segmentation in fluorescence microscopy images is achieved by applying Cellpose to process input TIFF files, detect cellular boundaries, and generate segmentation masks. This skill handles image preprocessing, segmentation, and metric extraction automatically.

Segmented cells yield per-cell morphology metrics including area, diameter, centroid, and eccentricity. The process outputs these measurements alongside visual overlay figures and a comprehensive report for analyzing fluorescence microscopy data.

Yes, the cell segmentation pipeline processes standard TIFF files for fluorescence microscopy images. It utilizes tifffile to read input images and applies Cellpose to detect and segment individual cells within the TIFF format.

The best way to generate visual overlays for cell segmentation results is to use matplotlib to map segmentation masks onto original microscopy images. This skill automatically produces these overlay figures to facilitate visual analysis of segmented cells.

Yes, you need Cellpose version 4.0 or higher installed to run fluorescence microscopy cell segmentation. The workflow also requires scikit-image, numpy, and tifffile to execute image preprocessing and cell boundary detection successfully.