Cellpose in Fiji Made Easy (No Python Needed!) [2D & 3D Segmentation]

Want to use Cellpose in Fiji without dealing with Python setup? You’re in the right place! In this beginner-friendly tutorial, I’ll show you how to run Cellpose directly in Fiji for both 2D and 3D segmentation—quickly and easily. You’ll learn how to perform accurate segmentation in Fiji (ImageJ) using the Cellpose-Appose plugin, which lets you use deep learning tools with minimal setup. This workflow is perfect if you’re new to image analysis or just want a simpler, hassle-free approach. The best part? No manual Python installation required. Cellpose-Appose automatically downloads and sets up a shared Python environment on your system, so you don’t need to install Anaconda, PyTorch, or anything else. 🔗 Get Cellpose-Appose here: https://imagej.net/plugins/cellpose-a... ⚠️ Note: This tutorial uses an early-stage development tool. While it’s still evolving, it’s already very powerful and useful for everyday workflows. Image credits: CIL13383: Karen Meaburn, Tom Misteli (2011) CIL:13383, Homo sapiens, mammary epithelium (MEC). CIL. Dataset. (RRID:SCR_003510). https://doi.org/doi:10.7295/W9CIL13383 CIL13384: Karen Meaburn, Tom Misteli (2011) CIL:13384, Homo sapiens, mammary epithelia (MEC). CIL. Dataset. (RRID:SCR_003510). https://doi.org/doi:10.7295/W9CIL13384 CIL39634: David Ball, Jean Peccoud (2012) CIL:39634, Saccharomyces cerevisiae. CIL. Dataset. (RRID:SCR_003510). https://doi.org/doi:10.7295/W9CIL39634 If you found this helpful, don’t forget to like the video and subscribe for more Fiji/ImageJ tutorials!    / @johanna.m.dela-cruz