Bioimage Analysis: Microscopy Image Analysis for Cell Biology

Overview

Bioimage Analysis provides computational tools for processing and quantifying microscopy images in cell biology research. From cell segmentation using deep learning (Cellpose) and classical methods (watershed, Otsu thresholding) to object tracking across time-lapse sequences, morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. This skill covers the full pipeline from raw microscopy images to quantitative measurements suitable for statistical analysis.

When to Use This Skill

• Segmenting cells or nuclei from brightfield or fluorescence microscopy images
• Tracking cell migration or particle movement in time-lapse sequences
• Quantifying cell morphology (area, eccentricity, circularity, aspect ratio)
• Counting bacterial or mammalian cell colonies on plates
• Analyzing colocalization of proteins from multi-channel fluorescence images
• Characterizing cytoskeleton fiber orientation and alignment
• Quantifying mitochondrial morphology from JC-1 or TMRM staining
• Batch processing multiple fields of view or multi-well plate images
• Preprocessing microscopy images (denoising, background subtraction, contrast enhancement)

Related Skills: For whole-slide pathology image analysis use pathml or histolab. For flow cytometry data use flow-cytometry-analysis. For clinical imaging (MRI, CT) use clinical-imaging.

Installation

uv pip install cellpose scikit-image opencv-python trackpy tifffile aicsimageio numpy pandas matplotlib

Quick Start

from cellpose import models
import skimage.io
import skimage.measure
import numpy as np

# Load image
image = skimage.io.imread('cells.tif')

# Segment with Cellpose
model = models.Cellpose(model_type='cyto2')
masks, flows, styles, diams = model.eval(image, diameter=None, channels=[0, 0])