Software

Here are the software we have developed or contributed to

NucleiSky (2026)

NucleiSky is a constellation-based registration tool for microscopy images in 2D and 3D. It aligns partial fields of view, ROIs, or tissue subvolumes to larger reference images by matching the spatial geometry of segmented nuclei, rather than relying on pixel-intensity similarity. NucleiSky supports browser-based Colab workflows, a no-code desktop GUI, and Python-based analysis pipelines.

Read our paper and find NucleiSky on GitHub.

LabConstrictor (2026)

LabConstrictor turns Jupyter notebooks into installable desktop applications. It helps researchers share analysis workflows as user-friendly apps that can be launched without Python, pip, or terminal commands, while keeping the underlying project versioned and reproducible. LabConstrictor can package notebooks, build cross-platform installers, and provide a simple welcome dashboard for end users.

Read our paper and find LabConstrictor on GitHub.

EZInput (2026)

EZInput is a Python library for building interactive user interfaces for scientific computing workflows. It lets developers define inputs once and reuse the same interface logic across Jupyter notebooks, Google Colab, and terminal environments. EZInput supports validated inputs, session persistence, and shareable parameter files, helping make computational tools easier to run and reproduce.

Read our paper and find EZInput on GitHub.

Rxiv-Maker (2025)

Rxiv-Maker is a framework for writing scientific manuscripts in Markdown and converting them into publication-ready PDFs. It supports automated figure generation, citation management, LaTeX typesetting, executable code blocks, DOCX export, and preprint submission packages, helping researchers keep manuscripts synchronized with their data, code, and version history.

Read our paper and find Rxiv-Maker on GitHub.

SReD (2025)

SReD, the Structural Repetition Detector, is an ImageJ/Fiji plugin for detecting and quantifying repetitive structures in microscopy images. It generates repetition maps using local texture similarity, enabling unbiased analysis of recurring biological structures across scales and imaging modalities without requiring labelled training data.

Read our paper and find SReD on GitHub.

PhotoFiTT (2024)

PhotoFiTT (Phototoxicity Fitness Time Trial) is an integrated framework combining a standardised experimental protocol with advanced image analysis to quantify light-induced cellular stress in label-free settings. PhotoFiTT leverages machine learning and cell cycle dynamics to analyse mitotic timing, cell size changes, and overall cellular activity in response to controlled light exposure.

Read our paper and find PhotoFiTT on GitHub.

CellTracksColab (2024)

CellTracksColab is an online platform tailored to simplify the exploration and analysis of tracking data by leveraging the free, cloud-based computational resources of Google Colab. CellTracksColab facilitates the amalgamation and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest).

Read our paper and find CellTracksColab on GitHub.

NanoPyx (2024)

NanoPyx is a library specialized in the analysis of light microscopy and super-resolution data. It is a successor to NanoJ, which is a Java library for the analysis of super-resolution microscopy data. NanoPyx focuses on performance, by heavily exploiting cython aided multiprocessing and simplicity. It implements methods for the bioimage analysis field, with a special emphasis on those developed by the Henriques Laboratory. It will be distributed as a Python Library and also as Codeless Jupyter Notebooks, that can be run locally or on Google Colab, and as a napari plugin.

Read our paper and find NanoPyx on GitHub.

DL4MicEverywhere (2024)

DL4MicEverywhere is a platform that offers researchers an easy-to-use gateway to cutting-edge deep learning techniques for bioimage analysis. It features interactive Jupyter notebooks with user-friendly graphical interfaces that require no coding skills. The platform utilizes Docker containers to ensure portability and reproducibility, guaranteeing smooth operation across various computing environments.

DL4MicEverywhere extends the capabilities of ZeroCostDL4Mic by allowing the execution of notebooks either locally on personal devices like laptops or remotely on diverse computing platforms, including workstations, high-performance computing (HPC), and cloud-based systems. It currently incorporates numerous pre-existing ZeroCostDL4Mic notebooks for tasks such as segmentation, reconstruction, and image translation.

Read our paper and find DL4MicEverywhere on GitHub.

eSRRF (2023)

eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

Read our paper and find eSRRF on GitHub.

Fast4DReg (2023)

Fast4DReg is a Fiji plugin for 2D and 3D video and can correct drift in all x-, y-, and z-directions. Fast4DReg creates intensity projections along both axes, estimates their drift using cross-correlation-based drift correction, and then translates the video frame by frame. Additionally, Fast4DReg can be used for aligning multi-channel 2D or 3D images, which is particularly useful for instruments that suffer from a misalignment of channels.

Read our paper and find Fast4DReg on GitHub.

TrackMate (2022)

TrackMate is your buddy for your everyday tracking.

TrackMate is an automated tracking software that analyzes bioimages and is distributed as a Fiji plugin.

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Read our article here.

Automated cell tracking using StarDist and TrackMate. Read our paper here.

See TrackMate in action here.

ZeroCostDL4Mic (2021)

ZeroCostDL4Mic allows the use of popular Deep Learning neural networks capable of carrying out tasks such as image segmentation and object detection (using U-Net, StarDist, and YOLOv2), image denoising and restoration (using CARE and, Noise2Void), super-resolution microscopy (using Deep-STORM) and image-to-image translations (using Label-free prediction fnet, pix2pix, and CycleGAN).

Read our paper and access the ZeroCostDL4Mic platform.

SRRF TFM (2020)

we propose a simplified protocol and imaging strategy that enhances the output of traction force microscopy by increasing i) achievable bead density and ii) the accuracy of bead tracking. Our approach relies on super-resolution microscopy, enabled by fluorescence fluctuation analysis. Our pipeline can be used on spinning-disk confocal or widefield microscopes and is compatible with available analysis software.

Read our paper.

FiloMap (2019)

ImageJ and R scripts used to map the localization of proteins within filopodia and generate filopodia maps.
Read our paper here.

Step-by-step protocols on how to use FiloMap to compare the localization of multiple proteins within filopodia.
Access our article here.

Find FiloMap on GitHub.

FiloQuant (2017)

The filopodia quantification plugin

FiloQuant is a user-friendly tool for the automated detection and quantification of filopodia properties such as length and density. We developed FiloQuant as a plugin for Fiji. Read our paper.

Step-by-step protocols on how to use FiloQuant to quantify filopodia
and filopodia-like protrusions from microscopy images. Access our article here.

Find FiloQuant on GitHub.