Labeling Cells with napari and Python: A Step-by-Step Guide for BioImage Analysis

Author(s): MicroBioscopicData (by Alexandros Athanasopoulos)

Originally published on Towards AI.

In this tutorial, we will go through a step-by-step guide on how to label cells using napari, an interactive multi-dimensional image viewer for Python, ideal for microscopy data. This hands-on guide is designed for biologists, data scientists, and image analysts, and will cover everything we need to know — from loading microscopy images into Python, to navigating napari’s labeling tools, and finally saving our labeled images in structured folders for downstream analysis or machine/deep learning.

We will work entirely within a Jupyter Notebook environment, combining Python scripting with visual exploration and annotation using napari. This tutorial is designed to be beginner-friendly but assumes that the reader has a basic understanding of microscopy, Python syntax, and how to work with Jupyter Notebooks, as well as a general familiarity with image segmentation concepts.

To work efficiently with our microscopy images — more precisely .lif files from Leica microscope — and to label them properly with napari, it’s essential to maintain a clear, well-structured folder system. In my case, I will use a 4-folder system to organize my project:

Raw .lif Files: This first folder will store the original .lif microscopy files, exactly as exported from my Leica microscope. This ensures that the raw data remains… Read the full blog for free on Medium.

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Published via Towards AI