Hannah S. Heil, Lund University, November 2025
This repository contains teaching materials, exercises, and resources for an Image Processing and Analysis lecture. It is intended for students, instructors, and researchers who want to learn or teach practical image analysis techniques, including segmentation, tracking, and quantification.
The repository includes:
- Jupyter/Colab notebooks with hands-on tutorials
- Supplementary resources (datasets, references, and reading material)
Click the “Open in Colab” badge or open directly via Colab:
Students should make a copy of the notebook in their own Google Drive to run and modify it independently.
Always work on your own copy of a notebook.
Explore the example images before modifying the analysis.
Read comments and markdown cells carefully—they contain explanations and hints.
Ask questions or report issues via the GitHub Issues tab.
| Tool | Description | Link |
|---|---|---|
| NEUBIAS | European network for bioimage analysis, offering courses, workshops, and tutorials. | NEUBIAS, NEUBIAS Training |
| Euro-BioImaging | European infrastructure providing access to imaging technologies and training. | Euro-BioImaging |
| Image.sc | Forum for image analysis discussion, plugin support, and community help. | Image.sc |
| Bio-image Analysis Notebooks | Collection of python notebooks for image processing and analysis. | Link |
| Tool | Description | Link |
|---|---|---|
| Fiji | Open-source image processing package with extensive plugins for microscopy and bioimage analysis. | Fiji |
| BioVoxxel Toolbox | Collection of ImageJ/Fiji plugins for advanced image processing tasks. | BioVoxxel Toolbox |
| Napari | Python-based interactive multi-dimensional image viewer with plugin support. | Napari |
| Google Colab | Cloud-based Jupyter notebook environment for running Python code and image analysis workflows. | Colab |
| Tool | Description | Link |
|---|---|---|
| ZeroCostDL4Mic | Framework for running deep learning models for microscopy on cloud platforms with minimal setup. | ZeroCostDL4Mic |
| BioImage Model Zoo | Repository of pre-trained deep learning models for microscopy image analysis. | bioimage.io |
| DeepImageJ | Integrates deep learning models into Fiji/ImageJ for microscopy analysis. | DeepImageJ |
| Tool | Description | Link |
|---|---|---|
| Nyquist Calculator | Tool for calculating Nyquist sampling rates to ensure proper image resolution. | Nyquist Calculator |
A curated and annotated list of key literature, tools, and resources.
Abbe, E. (1873). Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Archiv für Mikroskopische Anatomie, 9, 413–418.
➜ Classical optics; diffraction limit; foundations of microscopy
Appeltshauser, L., Linke, J., Heil, H. S., Karus, C., Schenk, J., Hemmen, K., Sommer, C., Doppler, K., & Heinze, K. G. (2023). Super-resolution imaging pinpoints the periodic ultrastructure at the human node of Ranvier and its disruption in patients with polyneuropathy. Neurobiology of Disease, 182, 106139. https://doi.org/10.1016/j.nbd.2023.106139
➜ Super-resolution microscopy; neurobiology application
Archit, A., Freckmann, L., Nair, S. et al. (2025). Segment Anything for Microscopy. Nature Methods, 22, 579–591. https://doi.org/10.1038/s41592-024-02580-4
➜ Foundation models; segmentation; SAM adaptation for microscopy
Brocher, J. (2025). BioVoxxel 3D Box (bv3dbox). Zenodo. https://doi.org/10.5281/ZENODO.17702242
➜ 3D image analysis; Fiji plugin
Gonzalez, R. C., & Woods, R. E. (2017). Digital image processing (Fourth edition, global edition). Pearson.
➜ Fundamental textbook; image processing theory and algorithms
Hinderling, L., Heil, H. S., Rates, A., et al. (2025). Smart Microscopy: Current Implementations and a Roadmap for Interoperability. https://doi.org/10.1101/2025.08.18.670881
➜ Smart microscopy; automation; hardware–software integration
Laine, R. F., Arganda-Carreras, I., Henriques, R., & Jacquemet, G. (2021). Avoiding a replication crisis in deep-learning-based bioimage analysis. Nature Methods, 18(10), 1136–1144. https://doi.org/10.1038/s41592-021-01284-3
➜ Reproducibility; deep learning best practices
Laine, R. F., Heil, H. S., Coelho, S., Nixon-Abell, J., Jimenez, A., Wiesner, T., … Henriques, R. (2023). High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation. Nature Methods, 20(12), 1949–1956. https://doi.org/10.1038/s41592-023-02057-w
➜ Live-cell nanoscopy; data-driven SRRF; computational super-resolution
napari contributors. (2019). napari: a multi-dimensional image viewer for Python. https://doi.org/10.5281/zenodo.3555620
➜ Visualization; Python viewer; plugin ecosystem
Ouyang, W., Beuttenmueller, F., Gómez-de-Mariscal, E., et al. (2022). BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis. bioRxiv. https://doi.org/10.1101/2022.06.07.495102
➜ Deep learning model repository; model standardization
Royer, L. A. (2024). Omega — harnessing the power of large language models for bioimage analysis. Nature Methods, 21, 1371–1373. https://doi.org/10.1038/s41592-024-02310-w
➜ LLMs for bioimage analysis; multimodal workflows
Uchida, S. (2013). Image processing and recognition for biological images. Development, Growth & Differentiation, 55, 523–549. https://doi.org/10.1111/dgd.12054
➜ Classical image processing; feature extraction; pattern recognition
von Chamier, L., Laine, R. F., & Henriques, R. (2019). Artificial intelligence for microscopy: what you should know. Biochemical Society Transactions, 47(4), 1029–1040. https://doi.org/10.1042/BST20180391
➜ Introductory review of AI for microscopy
von Chamier, L., Laine, R. F., Jukkala, J., Spahn, C., Krentzel, D., … Henriques, R. (2021). Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications, 12(1), 2276. https://doi.org/10.1038/s41467-021-22518-0
➜ Accessible DL training; Google Colab workflows
⚖️ License
This repository is shared for educational purposes. See LICENSE for details.