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FREE, USER-FRIENDLY PROTOCOLS for image processing
Many powerful image analysis tools are developed by computational scientists, but they are often difficult to access if you don’t have programming experience. As a biologist, this can make it challenging to fully benefit from the latest advances in image processing.
This page is designed to change that.
We focus on modern, state-of-the-art tools such as napari, ilastik, and other advanced software that go beyond traditional workflows. These platforms provide access to powerful methods including machine learning–based segmentation, multidimensional visualization, and integration of cutting-edge models like the Segment Anything Model.
Unlike basic tutorials that only cover widely used tools, we aim to guide you through more advanced approaches, while keeping everything accessible to non-coders.
We provide clear installation guides (step-by-step, no technical jargon) to make advanced image analysis usable for scientists who don’t code.
Napari
Napari
An open-source, Python-based graphical user interface (GUI) designed for multidimensional image visualization and analysis in bioimaging and microscopy.
Plugins https://napari-hub.org/index.html
Napari stands out for its extensive and constantly expanding plugin ecosystem, which enhances the core interface with powerful image processing and analysis tools. Plugins can be easily installed and combined to create flexible, reproducible workflows adapted to specific research needs.
Ilastik
Ilastik
An easy-to-use image analysis tool that uses machine learning to segment, classify, track, and count cells or other experimental data.
Organoid Tracker
Organoid Tracker
An open-source Python tool for tracking cell nuclei in time-lapse microscopy images of organoids, offering manual correction, automated detection with convolutional neural networks, and lineage tracking across time points.
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