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https://github.com/epfl-center-for-imaging/awesome-scientific-image-analysis

A curated list of scientific image analysis resources and software tools.
https://github.com/epfl-center-for-imaging/awesome-scientific-image-analysis

List: awesome-scientific-image-analysis

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A curated list of scientific image analysis resources and software tools.

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README 

        
![EPFL Center for Imaging logo](https://imaging.epfl.ch/resources/logo-for-gitlab.svg)

![epfl](./assets/epfl.jpeg)

# Awesome Scientific Image Analysis [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



A curated list of scientific image analysis resources and software tools.



## 🔖 Getting started



These are our favourite **resources** for learning the basics of image analysis:



- [Image data science with Python and Napari](https://biapol.github.io/Image-data-science-with-Python-and-Napari-EPFL2022/intro.html) - EPFL & TU Dresden

- [Image Processing and Analysis for Life Scientists](https://courseware.epfl.ch/courses/course-v1:EPFL+IPA4LS+2019_t3/about) - BIOP, EPFL

- [Introduction to Bioimage Analysis](https://bioimagebook.github.io/README.html) - Pete Bankheads



Here's a short list of image analysis **software** that we recommend:



- [Fiji](https://fiji.sc/) - ImageJ, with “batteries-included”

- [Ilastik](https://www.ilastik.org/) - Interactive learning and segmentation toolkit

- [Napari](https://napari.org/) - A fast and interactive multi-dimensional image viewer for Python

- [QuPath](https://qupath.github.io/) - Open Software for Bioimage Analysis



Read our **setup guide**:



- [Setting up Python for scientific image analysis](https://imaging.epfl.ch/field-guide/sections/python/notebooks/python_setup.html)



## 🧑‍🤝‍🧑 Communities



- [Image.sc](https://image.sc/)

- [GloBIAS](https://www.globias.org/)

- [SwissBIAS](https://swissbias.github.io/)



## 📚 Learning resources



**Curated lists**



- [Awesome Biological Image Analysis](https://github.com/hallvaaw/awesome-biological-image-analysis)

- [Awesome Computer Vision](https://github.com/jbhuang0604/awesome-computer-vision)

- [Awesome Medical Imaging](https://github.com/fepegar/awesome-medical-imaging)



**Guides**



- [Introduction to Bioimage Analysis](https://bioimagebook.github.io/README.html) - Pete Bankheads

- [Image data science with Python and Napari](https://biapol.github.io/Image-data-science-with-Python-and-Napari-EPFL2022/intro.html) - EPFL & TU Dresden

- [The Image Analysis Field Guide](https://imaging.epfl.ch/field-guide/) - EPFL Center for Imaging



**Courses**



- [Image Processing and Analysis for Life Scientists](https://courseware.epfl.ch/courses/course-v1:EPFL+IPA4LS+2019_t3/about) - BIOP, EPFL

- [Introduction to Programming in the Biological Sciences Bootcamp](https://justinbois.github.io/bootcamp/2022_epfl/#) - Justin Bois



**Papers**



- [A Hitchhiker's guide through the bio-image analysis software universe](https://febs.onlinelibrary.wiley.com/doi/full/10.1002/1873-3468.14451) - Robert Haase et al.

- [Towards effective adoption of novel image analysis methods](https://www.nature.com/articles/s41592-023-01910-2) - Talley Lambert, Jennifer Waters

- [Creating and troubleshooting microscopy analysis workflows: Common challenges and common solutions](https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.13288) - Beth Cimini



**Video series**



- [Introduction to bioimage analysis](https://www.youtube.com/watch?v=e-2DbkUwKk4&list=PL5ESQNfM5lc7SAMstEu082ivW4BDMvd0U&index=3)

- [First principles in computer vision](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw)

- [DigitalSreeni](https://www.youtube.com/c/DigitalSreeni)

- [Microscopy Series](https://www.ibiology.org/online-biology-courses/microscopy-series/)

- [Microcourses](https://www.youtube.com/@Microcourses/videos)



## ✂️ Image segmentation



Image segmentation aims to create a segmentation mask that identifies specific classes or objects. Techniques for image segmentation include thresholding, weakly supervised learning (e.g., Ilastik, Weka), and deep learning.



**Learning resources**



- [Thresholding - Introduction to Bioimage Analysis](https://bioimagebook.github.io/chapters/2-processing/3-thresholding/thresholding.html)

- [Thresholding - Scikit-image](https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_thresholding.html)

- [Segmentation - ImageJ Tutorials](https://imagej.net/imaging/segmentation)

- [Image segmentation - Image data science with Python and Napari](https://biapol.github.io/Image-data-science-with-Python-and-Napari-EPFL2022/day2d_image_segmentation/readme.html)



**Software tools**



- [skimage.segmentation](https://scikit-image.org/docs/stable/api/skimage.segmentation.html)

- [Ilastik - Pixel Classification](https://www.ilastik.org/documentation/pixelclassification/pixelclassification)

- [Segment Anything Model 2 (SAM 2)](https://github.com/facebookresearch/sam2)

- [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)

- [rembg](https://github.com/danielgatis/rembg)

- [nnUNet](https://github.com/MIC-DKFZ/nnUNet)

- [segmentation_models](https://github.com/qubvel/segmentation_models)

- [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)

- [Monai](https://github.com/Project-MONAI/MONAI)

- [StarDist](https://github.com/stardist/stardist)

- [CellPose](https://github.com/mouseland/cellpose)



## 📐 Image registration



Image registration is used to align multiple images, stabilize sequences by compensating for camera movement, track object movement and deformation, and stitch multiple fields of view together.



**Learning resources**



- [Image correlation - Practice](https://www.spam-project.dev/docs/)

- [Image correlation - Theory](https://www.spam-project.dev/docs/)



**Software tools**



- [skimage.registration](https://scikit-image.org/docs/stable/api/skimage.registration.html)

- [SPAM](https://www.spam-project.dev/)

- [pystackreg](https://github.com/glichtner/pystackreg)

- [TurboReg](https://bigwww.epfl.ch/thevenaz/turboreg/)

- [Warpy](https://imagej.net/plugins/bdv/warpy/warpy)

- [ABBA](https://github.com/BIOP/ijp-imagetoatlas)



## 🪄 Image denoising



Image denoising enhances visual quality by removing noise, making structures more distinguishable and facilitating segmentation through thresholding.



**Learning resources**



- [Noise - Introduction to Bioimage Analysis](https://bioimagebook.github.io/chapters/3-fluorescence/3-formation_noise/formation_noise.html)

- [Denoising a picture (scikit-image)](https://scikit-image.org/docs/stable/auto_examples/filters/plot_denoise.html)



**Software tools**



- [skimage.restoration](https://scikit-image.org/docs/stable/api/skimage.restoration.html)

- [CAREamics](https://github.com/CAREamics/careamics)



## 🔍 Object detection



Object detection is the process of identifying and localizing objects within an image or video using various shapes such as bounding boxes, keypoints, circles, or other geometric representations.



**Software tools**



- [Ultralytics YOLO](https://github.com/ultralytics/ultralytics)

- [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut)

- [OpenPifPaf](https://github.com/openpifpaf/openpifpaf)

- [Spotiflow](https://github.com/weigertlab/spotiflow)



## 🐾 Tracking



Object tracking is the process of following objects across time in a video or image time series.



**Learning resources**



- [Single cell tracking with napari](https://napari.org/stable/tutorials/tracking/cell_tracking.html)

- [Walkthrough (trackpy)](https://soft-matter.github.io/trackpy/dev/tutorial/walkthrough.html)

- [Getting started with TrackMate](https://imagej.net/plugins/trackmate/tutorials/getting-started)



**Software tools**



- [TrackMate](https://imagej.net/plugins/trackmate/)

- [Trackpy](https://github.com/soft-matter/trackpy)

- [Trackastra](https://github.com/weigertlab/trackastra)

- [ultrack](https://github.com/royerlab/ultrack)

- [co-tracker](https://github.com/facebookresearch/co-tracker)

- [LapTrack](https://github.com/yfukai/laptrack)



## 🌻 Visualization



A variety of software tools are available for visualizing scientific images and their associated data.



**Learning resources**



- [Visual image comparison (Scikit-image)](https://scikit-image.org/docs/stable/auto_examples/applications/plot_image_comparison.html#sphx-glr-auto-examples-applications-plot-image-comparison-py)



**Software tools**



- [Fiji](https://fiji.sc/)

- [Napari](https://napari.org/stable/)

- [QuPath](https://qupath.github.io/)

- [Paraview](https://www.paraview.org/)

- [Neuroglancer](https://github.com/google/neuroglancer)

- [pyvista](https://pyvista.org/)

- [vedo](https://github.com/marcomusy/vedo)

- [tif2blender](https://github.com/oanegros/tif2blender)

- [NeuroMorph](https://github.com/NeuroMorph-EPFL/NeuroMorph)

- [BigDataViewer](https://imagej.net/plugins/bdv/)



## 🔋 Performance



Performance optimization is the process of making code execution faster, more efficient, or using fewer computing resources.



**Learning resources**



- [System aspects - Basics of Computing Environments for Scientists](https://compenv.phys.ethz.ch/system_aspects/)

- [Accelerated large-scale image procesing in Python](https://github.com/EPFL-Center-for-Imaging/accel-large-image-proc-talk)



**Software tools**



- [pyclesperanto_prototype](https://github.com/clEsperanto/pyclesperanto_prototype)

- [Numba](https://numba.pydata.org/)

- [cuCIM](https://github.com/rapidsai/cucim)

- [OpenCV](https://opencv.org/)



## 🕊️ Open science



Open imaging science meets principles of findability, accessibility, interoperability, and reusability (FAIR).



**Software development practices**



- [The Turing Way handbook](https://the-turing-way.netlify.app/index.html)

- [Code Publishing cheat sheet](https://www.epfl.ch/schools/enac/wp-content/uploads/2022/06/ENAC-IT4R_Code_Publishing_Cheat_Sheet.pdf)

- [Good enough practices in scientific computing](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510)



**Reproducibility**



- [Reproducible image handling and analysis](https://www.embopress.org/doi/full/10.15252/embj.2020105889)

- [Understanding metric-related pitfalls in image analysis validation](https://arxiv.org/abs/2302.01790)

- [Reporting reproducible imaging protocols](https://www.sciencedirect.com/science/article/pii/S2666166722009194?via%3Dihub)

- [When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis](https://www.nature.com/articles/s41592-023-01881-4)



**Figures creation**



- [Community-developed checklists for publishing images and image analysis](https://arxiv.org/abs/2302.07005)

- [Creating Clear and Informative Image-based Figures for Scientific Publications](https://www.biorxiv.org/content/10.1101/2020.10.08.327718v2)

- [Effective image visualization for publications – a workflow using open access tools and concepts](https://f1000research.com/articles/9-1373)



## 🐍 Python



Python is a popular programming language for scientific image analysis.



**Python setup**



- [Setting up Python for scientific image analysis](https://imaging.epfl.ch/field-guide/sections/python/notebooks/python_setup.html)

- [Managing Conda Environments](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)

- [Conda Cheatsheet](https://docs.conda.io/projects/conda/en/latest/_downloads/843d9e0198f2a193a3484886fa28163c/conda-cheatsheet.pdf)

- [Python environments workshop](https://hackmd.io/@talley/SJB_lObBi) - Talley Lambert



**Python programming**



- [Python 3 documentation](https://docs.python.org/3/)

- [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/)

- [Programming with Python](https://swcarpentry.github.io/python-novice-inflammation/index.html) - Software Carpentry

- [pydevtips: Python Development Tips](https://pydevtips.readthedocs.io/en/latest/index.html) - Eric Bezzam

- [Python packaging 101](https://www.pyopensci.org/python-package-guide/tutorials/intro.html)



**Python for image processing**



- [Image processing with Python](https://github.com/guiwitz/Python_image_processing) - Guillaume Witz

- [User Guide](https://scikit-image.org/docs/stable/) - Scikit-image

- [3.3. Scikit-image: image processing](https://scipy-lectures.org/packages/scikit-image/index.html) - Scientific Python Lectures

- [Image processing with Python](https://datacarpentry.org/image-processing/) - Data Carpentry



## 🔬 Fiji (ImageJ)



Fiji is an open-source software for image processing and analysis. A wide range of community-developed plugins can enhance its functionality.



**Learning resources**



- [Scientific Imaging Tutorials](https://imagej.net/imaging/index) - ImageJ

- [Image handling using Fiji - training materials](https://zenodo.org/records/14771563) - Joanna Pylvänäinen



**Plugins**



- [ThunderSTORM](https://github.com/zitmen/thunderstorm)

- [MorphoLibJ](https://imagej.net/plugins/morpholibj)

- [OrientationJ](https://bigwww.epfl.ch/demo/orientationj/)



## 🏝️ Napari



Napari is a fast and interactive multi-dimensional image viewer for Python. It can be used for browsing, annotating, and analyzing scientific images.



**Learning resources**



- [napari.org](https://napari.org/stable/)



**Plugins**



- [napari-animation](https://github.com/napari/napari-animation)

- [napari-skimage-regionprops](https://github.com/haesleinhuepf/napari-skimage-regionprops)

- [Omega](https://github.com/royerlab/napari-chatgpt)

- [napari-threedee](https://github.com/napari-threedee/napari-threedee)



## 🧬 QuPath



QuPath is an open software for bioimage analysis, often used to process and visualize digital pathology and whole slide images.



**Learning resources**



- [qupath.github.io](https://qupath.github.io)



**Extensions**



- [qupath-extension-cellpose](https://github.com/BIOP/qupath-extension-cellpose)

- [qupath-extension-stardist](https://github.com/qupath/qupath-extension-stardist)

- [qupath-extension-sam](https://github.com/ksugar/qupath-extension-sam)



## 🛸 Other



**🤖 LLMs**



- [bia-bob](https://github.com/haesleinhuepf/bia-bob)

- [BioImage.IO Chatbot](https://github.com/bioimage-io/bioimageio-chatbot)



**📷 Image acquisition**



- [Cameras and Lenses](https://ciechanow.ski/cameras-and-lenses/)

- [Knowledge Center](https://www.edmundoptics.eu/knowledge-center) - Edmund Optics



**🩻 Image reconstruction**



- [Pyxu](https://pyxu-org.github.io/)



**👓 Depth estimation**



- [Depth Anything](https://github.com/LiheYoung/Depth-Anything)



**💲 Splines**



- [SplineBox](https://splinebox.readthedocs.io/en/latest/index.html)



**🍭 Orientation**



- [OrientationPy](https://epfl-center-for-imaging.gitlab.io/orientationpy/introduction.html)



**📏 Measurements**



- [pixelflow](https://github.com/alan-turing-institute/pixelflow)