Software Tutorials (with Video Guides)

ImageJ is a free, open-source image analysis software developed by the NIH.
It is widely used in biomedical research for tasks such as image processing, segmentation, particle analysis, and intensity measurements. With its plugin-based architecture and macro scripting support, ImageJ is highly extensible and well-suited for customized workflows.

Python is a versatile and widely used programming language in scientific computing, automation, and data analysis. With its rich ecosystem of open-source libraries, Python provides powerful tools for image processing, visualization, and machine learning. It is particularly suitable for building reproducible pipelines and integrating different types of experimental data.

napari is a fast, interactive Python viewer for multi-dimensional images. Backed by an expanding plugin ecosystem and an active community, it delivers robust tools for image processing, visualization, and quantification. It excels at seamlessly combining data exploration, computation, annotation, and analysis into a unified workflow.

Imaris is a commercial software for 3D and 4D visualization and analysis of microscopy data. Designed for researchers working with confocal, light sheet, and other volumetric imaging techniques, Imaris provides advanced tools for surface rendering, tracking, colocalization, and statistical analysis. It offers an intuitive interface and powerful performance for large datasets.

Arivis Vision4D is a high-performance platform for handling, visualizing, and analyzing large-scale multi-dimensional image data.
Especially suited for light sheet and whole-organ imaging, Arivis offers scalable 3D/4D visualization, object tracking, machine learning-based segmentation, and GPU-accelerated performance. It is widely adopted in digital pathology, neuroscience, and developmental biology.

QuPath is an open-source software platform for bioimage analysis, specifically designed for digital pathology and whole slide image (WSI) analysis.
It offers powerful tools for tissue detection, cell segmentation, biomarker quantification, and machine learning-based classification. QuPath supports a wide range of image formats and integrates with external tools such as ImageJ, Python, and OpenCV, making it highly extensible for both research and clinical applications.

Huygens is a professional software suite developed by Scientific Volume Imaging (SVI) for deconvolution and restoration of microscopy images.
It supports a wide range of modalities including confocal, widefield, STED, and light sheet microscopy. Huygens offers advanced deconvolution algorithms, 3D visualization, and quantitative analysis, making it ideal for improving image resolution and signal-to-noise ratio.

MetaXpress is a high-content image acquisition and analysis software developed by Molecular Devices.
It is widely used in automated microscopy for high-throughput screening, especially in drug discovery and cell-based assays. MetaXpress offers powerful image analysis modules, machine learning capabilities, and seamless integration with automated imaging systems.

MetaMorph is a comprehensive image acquisition and analysis software developed by Molecular Devices.
It is widely used for controlling microscopes, cameras, and stage devices in fluorescence and live-cell imaging. MetaMorph provides tools for time-lapse acquisition, ratiometric imaging, multi-channel acquisition, and quantitative analysis, making it ideal for research in cell biology and physiology.

ZEN is ZEISS’s proprietary software platform for microscope control, image acquisition, and analysis.
Designed for a wide range of ZEISS microscopy systems, ZEN supports confocal, super-resolution, and widefield imaging. It provides intuitive workflows, AI-based segmentation tools, 3D rendering, and experiment automation, making it highly versatile for both basic and advanced imaging applications.

LAS X is Leica Microsystems’ imaging software for microscope system control and multidimensional data analysis.
It supports a full range of modalities including confocal, STED, and light-sheet microscopy. LAS X enables users to design complex experiments with ease, perform real-time image analysis, and manage multi-dimensional datasets for both live and fixed samples.

Cellpose is a deep learning–based cell segmentation software that enables accurate identification of cell boundaries across diverse imaging conditions. It uses a neural network trained on a broad range of cell types to generate robust cell masks with minimal parameter tuning. The software performs well on cells with complex morphologies and heterogeneous intensities, making it a widely used tool for automated bioimage analysis.

StarDist is a deep learning–based image segmentation tool specifically designed for detecting and segmenting cell nuclei and other star-convex objects. It represents object boundaries as radial distances from their centers, enabling accurate separation of densely packed or touching objects. Trained models can be applied to a wide range of microscopy images with minimal parameter adjustment. StarDist is widely used in bioimage analysis for its high segmentation accuracy and user-friendly integration with Fiji and Python-based workflows.

ilastik is an interactive machine learning–based image analysis platform that enables image segmentation, classification, and object detection without requiring programming expertise. Users train pixel classifiers by providing simple annotations, allowing the software to distinguish structures of interest based on image features such as intensity, texture, and edge information. The trained classifier can then be applied to large image datasets to generate segmentation masks efficiently. Its intuitive interface and reproducible workflows have made ilastik a widely used tool in bioimage analysis.

microSAM is a microscopy image segmentation tool built upon the Segment Anything Model (SAM), a foundation model trained on a large and diverse collection of images. It enables rapid segmentation through interactive prompts, such as points, bounding boxes, or existing masks, while requiring minimal task-specific training. microSAM adapts SAM for biological imaging applications and supports a wide range of microscopy modalities. Its combination of flexibility, accuracy, and user-friendly workflows makes it a valuable tool for semi-automated bioimage analysis.

ZeroCostDL4Mic is an open-access platform that enables researchers to train and apply deep learning models for microscopy image analysis without requiring advanced programming skills or specialized hardware. Built on Google Colab, it provides ready-to-use notebooks for tasks such as image segmentation, restoration, denoising, and object detection. The platform simplifies the deployment of state-of-the-art deep learning methods through a user-friendly workflow. By lowering technical barriers, ZeroCostDL4Mic has made deep learning–based bioimage analysis more accessible to the broader life science community.

BioImage.IO is an open-source platform that facilitates the sharing, discovery, and deployment of deep learning models for bioimage analysis. It provides a standardized model format that enables interoperability across different image analysis software and workflows. Researchers can access a growing repository of pretrained models for tasks such as segmentation, denoising, restoration, and object detection. The platform aims to lower the barrier to applying artificial intelligence in microscopy by making advanced models more accessible and reproducible. Through community-driven development, BioImage.IO promotes the FAIR principles of scientific software and data sharing.

DeepImageJ is an open-source plugin that brings deep learning capabilities to ImageJ and Fiji. It enables users to apply pretrained neural networks for tasks such as image restoration, segmentation, and classification without requiring coding skills. The platform supports models developed in different deep learning frameworks and promotes interoperability across bioimage analysis tools. By integrating artificial intelligence into established image analysis workflows, DeepImageJ makes advanced image processing more accessible to researchers.