# A Hitchhiker's guide through the bio-image analysis software universe

Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Siân Culley, Ilya

Belevich, Eija Jokitalo, Martin Schorb, Anna Klemm, Christian Tischer

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Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Siân Culley, et al. A Hitchhiker's guide through the bio-image analysis software universe. FEBS Letters, 2022, 596 (19), pp.2472-2485. 10.1002/1873-3468.14451 . hal-03757709

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# A Hitchhiker's guide through the bio-image analysis software universe

Robert Haase¹,² ID, Elnaz Fazeli³ ID, David Legland⁴,⁵ ID, Michael Doube⁶ ID, Siân Culley⁷ ID, Ilya Belevich⁸ ID, Eija Jokitalo⁸ ID, Martin Schorb⁹,¹⁰ ID, Anna Klemm¹¹ ID and Christian Tischer¹⁰ ID

1 DFG Cluster of Excellence “Physics of Life”, TU Dresden, Germany

2 Center for Systems Biology Dresden, Germany

3 Biomedicum Imaging Unit, Faculty of Medicine and HiLIFE, University of Helsinki, Finland

4 INRAE, UR BIA, Nantes, France

5 INRAE, PROBE Research Infrastructure, BIBS Facility, Nantes, France

6 Department of Infectious Diseases and Public Health, City University of Hong Kong, Kowloon, Hong Kong

7 Randall Centre for Cell & Molecular Biophysics, King's College London, UK

8 Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Finland

9 Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany

10 Centre for Bioimage Analysis, European Molecular Biology Laboratory, Heidelberg, Germany

11 VI2 – Department of Information Technology and SciLifeLab Biolmage Informatics Facility, Uppsala University, Sweden

**Correspondence**

R. Haase, DFG Cluster of Excellence
“Physics of Life”, TU Dresden, 01307
Dresden, Germany

E-mail: robert.haase@tu-dresden.de

(Received 1 February 2022, revised 1 May 2022, accepted 12 May 2022), available online xx xxxx xxxx)

doi:10.1002/1873-3468.14451

Edited by Jan Borst

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

**Keywords:** bio-image analysis; open-source; software

Scientific bio-image analysis software plays a key role in modern life sciences [1]. New insights are virtually impossible without computational methods for image acquisition, processing, segmentation, feature extraction and visualisation. In the past decade, biologists have increasingly applied statistical data analysis of imaging data and machine learning for image processing and particularly for image segmentation, as these allow overcoming the limitations of purely descriptive

methods. We also perceive that tools and methods are converging: if a single software platform provides image processing, feature extraction, statistical analysis and visualisation, it is superior and preferred to software that is only good at one of those tasks, at least from a user's perspective. Possible applications are highly diverse and spread across multiple sub-disciplines such as developmental biology, cancer research, immunology, cell and molecular biology,

**Abbreviations**

ANT, advanced normalisation tools; cryoEM, cryo-electron microscopy; CSV, comma-separated values; GUI, graphical user interface; ITK, Insight Toolkit; JSON, JavaScript object notation; NEUBIAS, Network or European Bio-Image Analysts; OpenCV, open computer vision; RELION, REgularised Likelihood OptimisationN; XML, extensible markup language; YAML, yet another markup language.
biophysics, agronomy, bioengineering and biomateri-
als. Often software solutions are created to address a
particular analysis challenge in one of those sub-
disciplines. As it becomes increasingly hard to keep an
overview of existing software, corresponding key appli-
cations and targeted scientific questions, we provide a
detailed overview of current state-of-the-art software,
upcoming next-generation tools and give hints as to
which aspects to consider when deciding among the
many available software solutions for current bio-
image analysis questions.

An early career scientist searching for the right soft-
ware for their image analysis might have the hardest
decision to make. Even if most of them know search
engines specialised for bio-image analysis software such
as https://bii.eu or https://bio.tools [2,3], for beginners
in the field it is hard to make any decisions as they often
do not know the right search terms yet. Hence, the glos-
sary provided below may be a good starting point to get
an overview of available software and related use-cases.
Furthermore, we recommend attending institutional
image-analysis courses, for example, for PhD students
in their first year. In addition, getting in touch with
senior scientists in their own group, with collaborators
and local light or electron microscopy facilities is a good
opportunity to find out which software is used in similar
projects on campus.

Glossary

Inspired by Adams and Lloyd [4] we give an overview
of the bio-image analysis software universe by means
of a glossary of software routinely used by bio-image
analysts. To further classify those software-related
terms, we refer to previously defined groups of soft-
ware [5]: Firstly, image/data analysis algorithms pro-
vided in a sustainably reusable fashion are referred to
as ‘components’. Secondly, software libraries and stan-
dalone applications that combine multiple components
are ‘collections’. Thirdly, software that combines mul-
tiple components, potentially from multiple collections
to solve a given variety of image analysis questions in
a standardised form are referred to as ‘ workflow tem-
plates’. If the software is specific for solving particular
scientific questions using given components in one
specific assembly, these are called ‘workflows’. We
extend this classification with ‘frameworks’ of scientific
software which are collections upon which many other
software solutions are built. We add ‘programming
languages’ that allow assembling components into
workflows. We furthermore categorise the presented
software in additional categories such as open-source,
free of charge and major application categories such as

acquisition, registration, segmentation and statistical
analysis in Table S1. The table also contains properties
of the listed software such as preferred dimensionality
of input image data and typical imaging modality. The
software in the following were selected to reflect long-
term available, reliable, sustainably maintained and
supported software solutions. We analysts often have
to have a conservative perspective on existing software
as we need to rely on established, reliable and main-
tained software to build workflows for our collabora-
tors and trust the given software to be still available in
5–10 years allowing reproducible image data analysis.
To this end, the number of citations served as a crite-
rion to select software but we also considered software
packages that have been available for about 5–
10 years with continuous maintenance and reliable
support by a vivid community. A less formal criterion
that we applied for selecting software was considering
the knowledge of which tools would have allowed a
starting bio-image analyst to follow a conversation at
one of the Network of European Bio-Image Analysts
(NEUBIAS) meetings. The given description of the
glossary items highlights the main application of the
software and its relationships with other glossary
items. While the glossary focuses on general software
and terms used in the field, this should not hide the
large number of software or plugins developed for a
specific task or context. In the domain of plant science,
the quantitative plant initiative [6] proposes a curated
list of software solutions that may also be useful to
know. Similarly, in the microscope hardware and con-
trol oriented context, a list of useful software was pub-
lished [7]. Also for lightsheet microscopy, there is a
specific list of software for acquisition and analysis
available [8]. There are a large number of software
tools and applications that have been specifically
developed for the cryo-electron microscopy (cryoEM);
a comprehensive list of software for the cryoEM com-
munity can be found from wikibooks [9].

**3D IMAGE SUITE** [10] is a collection of IMAGE plugins for filtering, segmentation and analysis of geometry, shape and spatial organisation of objects in 3D images.

**3D SLICER** [11] is an image processing software based on the ITK library focused on medical imaging with 3D surface extraction, rendering and analysis capabilities. It is increasingly used for visualising and analysing 3D structures such as cells, tissues and organs in microscopy data.

ANTS [12], or Advanced Normalisation Tools, is a
collection of methods for image registration, segmenta-
tion, and analysis, mostly developed in the context of
neuro-imaging and the comparison of cohorts. ANTS
depends on `ITK`, an image processing library to which
ANTs developers contribute.

**ARIVIS VISION 4D** (Arivis AG, Rostock, Germany) is an image analysis software for processing multi-channel 2D, 3D and 4D data, focused but not limited to microscopy data. It is scalable, supports processing big image data, and has intuitive image stitching and alignment tools.

**AMIRA-AVIZO** (Thermo Fisher Scientific Inc., Waltham, MA, USA) is a 2D–5D image processing, visualisation and analysis software. It can be customised using `PYTHON` and `MATLAB` and offers additions for incorporating artificial intelligence.

**BIGDATAVIEWER** [13] is an *n*-dimensional image viewer component for slicing volumes in arbitrary directions. The `FIJI` plugin can handle terabyte-sized image data composed of multiple channels and time points.

**BIGSTITCHER** [14] is an automated and interactive image registration/fusion `FIJI` plugin capable of handling terabyte sized image data. It is based on the `BIGDATAVIEWER`.

**BIOFORMATS** [15] is an image file format interoperability library which serves multiple image analysis software applications such as `FIJI`, `OMERO` and `QUPATH`, and programming environments such as `MATLAB`, `JAVA` and `PYTHON` to load image data from many formats and vendors.

**BLENDER** [16] is a 3D surface rendering, modelling and visualisation software with `PYTHON` scripting, simulation and video editing capabilities. The home of `BLENDER` is in design and arts, and it is increasingly used for microscopy image data visualisation.

**BONEJ** [17] is a collection of image processing operations and `IMAGEJ` plugins for skeletal/bone image analysis. It is used often in the soil, food and materials science communities. Some of the tools were updated to work with `IMAGEJ2`.

`c/c++` are programming languages traditionally used in computing. Most operating systems are programmed in `c` and `c++`. Furthermore, many `PYTHON` and also some `JAVA` libraries contain components and collections of processing routines written in these languages because `c` and `c++` offer higher performance.

**CATMAID** [18] is a web application to navigate, share and collaboratively annotate massive volume image data sets.

**CCP-EM** [19] the Collaborative Computational Project for electron cryo-microscopy is a community guiding the users of `CRYO-EM` software tools as well as developers of software packages and file formats.

**CCPI** [20] the Collaborative Computational Project in Tomographic Imaging provides a collection of

software tools for tomographic imaging and reconstruction.

**CELLPOSE** [21] is a deep-learning based segmentation algorithm for biological structures such as cell and cell nuclei in microscopy images. It is accessible as a `PYTHON` library and standalone application. `CellPose` plugins exist for `CELLPROFILER`, `QUPATH` and `FIJI`.

**CELLPROFILER** [22] is an image analysis software application with graphical user interface (GUI) for user-friendly configuration of standardised image analysis workflows focusing on high-throughput microscopy imaging data of cells with capabilities for extracting tabular image feature data in high-performance-computing environments.

**CELLPROFILER ANALYST** [23] is a data exploration software for further visualisation and analysis of tabular data produced with `CELLPROFILER`. It offers advanced plotting, dimensionality reduction and machine learning based object classification for dealing with big data as it is common in pharmaceutical research.

**DECONVOLUTIONLAB2** [24] is a collection of image deconvolution algorithms accessible as standalone command-line interface and as user-friendly `IMAGEJ` plugin.

**DRAGONFLY** (ORS, Montréal, QC, Canada) is a powerful standalone software featuring an extensive set of tools for image processing, segmentation and 3D visualisation.

**DRISHTI** [25,26] is a visualisation tool for 3D pixel data, which has been extended with segmentation and measurement tools.

**ELASTIX** [27] is a standalone command-line tool for registration of 2D and 3D image data based on the `ITK` library. A `PYTHON` compatible interface, SimpleElastix, is available as well.

**EMAN2** [28] is a software application focusing on cryoEM, covering techniques such as single particle analysis, cryo-electron tomography or sub-tomogram averaging.

**FAIRSIM** [29] is an `IMAGEJ` plugin for reconstructing structured illumination microscopy super-resolution images from raw data.

**FIJI** [30] is an image analysis software based on `IMAGEJ` and a collection of `IMAGEJ-` and `IMAGEJ2` compatible plugins focusing on general-purpose image analysis in the life sciences. It is scriptable using multiple programming languages compatible with the Java ecosystem, extensible and capable of handling big image data through integration of components such as `IMGLIB2` and `BIGDATAVIEWER`.

**GROOVY** [31] is a scripting language that can be used for automating image analysis routines in `QUPATH` and `FIJI`.
**GWYDDION** [32] is a modular program for scanning
probe microscopy data visualisation and analysis, pri-
marily focused on the analysis of altitude maps such
as obtained by atomic force microscopes.

**HUYGENS** (Scientific Volume Imaging B.V., Hilver-
sum, the Netherlands) is an image processing software
dedicated to deconvolution of 3D stacks from fluores-
cence microscopy, potentially multi-channel and time-
lapse data.

**ICY** [33] is an image analysis software focusing on
general purpose image analyses in the life sciences
compatible with **IMAGEJ**. **ICY** is scriptable using Java-
Script and a visual programming approach using so
called protocols.

**ILASTIK** [34] is an image analysis software offering
easy-to-use machine learning capabilities for image
segmentation, object classification, object tracking and
statistical analysis of microscopy image data. Ilastik
classifiers can be used from **FIJI** and **CELLPROFILER**. Fur-
thermore, it supports execution on high-performance-
computing clusters.

**IMAGEJ** [35] is an image analysis software and frame-
work for image analysis algorithms integrated in **FIJI**,
**ICY**, **MICROMANAGER**, **QUPATH** and others. We conserva-
tively estimate tens or hundreds of thousands of plug-
ins and scripts have been developed in its 20+ year
history making it one of the most important platforms
for image analysis in the life sciences.

**IMAGE J2** [36] is a modern rewrite of the **IMAGEJ** code-
base with focus in scientific image processing and anal-
ysis of big image data. It serves as an extensible
platform underlying **FIJI** and other software platforms
in the life sciences.

**IMAGEJ MACRO** [37] is a limited programming lan-
guage specific to the **IMAGEJ** platform useful for
automating image processing routines.

**IMAGE.SC** [38] is an online discussion forum based on
the Discourse platform [39] that serves as a questions
and answers forum for many open source projects
from the image science field. It plays a key role in
knowledge exchange and community support for many
open source bio-image analysis software projects. See
Fig. 1 for a list of community partners.

**IMARIS** (Oxford Instruments, Oxon, UK) is an image
processing and visualisation software supporting 3D
volume rendering and quantitative analysis. Through
extra modules it is interoperable with **FIJI**, **PYTHON** and
**MATLAB**.

**IMGLIB2** [40] is an image processing framework and
collection of algorithms. It is the basis for software
such as **BIGDATAVIEWER**, **BIGSTITCHER**, **IMAGEJ2**, **FIJI**, **KN-
IME** and others to handle terabyte-sized big image
data.

**IMOD** [41] is an image processing, modelling and
visualisation software collection for electron micro-
scopy. Aside from command line tools for image pro-
cessing, it offers a GUI for reconstruction, registration
and segmentation of data.

**ITK** [42] is an image registration and segmentation
algorithm collection and library with a long history in
medical imaging. It is the underlying framework for
tools such as 3D SLICER, ELASTIX, ANTS, and **ITK SNAP**.

**ITK SNAP** [43] is a software application specifically for
segmentation and surface rendering of 3D medical
imaging datasets based on **ITK**.

**JAVA** is the programming language **ICY**, **IMAGEJ**, **FIJI**,
**QUPATH** and compatible plugins are written in. It is
also interoperable with **IMARIS** and **MATLAB**.

**JAVASCRIPT** is a scripting language used for automa-
tion of image analysis routines in **ICY**, **IMAGEJ** and **FIJI**.
It is also the most popular web programming language
world wide [44].

**JUPYTER NOTEBOOKS** [45] is an interactive, cloud com-
patible programming environment suitable for image
data analysis, statistics and scientific plotting. It is a
key component for reproducible data science in the sci-
entific **PYTHON** ecosystem and is extensively used for
documentation and training.

**JYTHON** is a Java-compatible scripting language
based on the syntax of **PYTHON 2**. It can be used for
automation of image analysis routines in **FIJI** but is
technically not compatible with **NUMPY**, **SCIPY**, **SCIKIT-
IMAGE** and other **PYTHON-based** libraries. It is compati-
ble with Java-based components.

**KNIME** [46] is a visual and interactive programming
environment focusing on data science with image anal-
ysis and machine learning capabilities. Its image pro-
cessing capabilities are based on **IMAGEJ**, **IMAGEJ2**,
**SCIJAVA** and **IMGLIB2**.

**KNOSSOS** [47] is an image visualisation and annota-
tion software for large connectomics (electron micro-
scopy) data extensible using **PYTHON** modules.

**LEICA APPLICATION SUITE X** (Leica Microsystems
GmbH, Wetzlar, Germany) is a software for micro-
scope control, image acquisition, visualisation and
analysis. It offers modules for computational clearing
and deconvolution (lightning), Fluorescence lifetime,
FRET, and FCS analysis, CARS calculations, 2D and
3D measurements.

**MATLAB** (Mathworks, Natick, MA, USA) is a soft-
ware environment for numeric computing that pro-
vides a multi-paradigm programming language and a
number of dedicated applications and toolboxes, for
example, for image processing, computer vision, statis-
tics and machine learning. It can be extended using
Java and Python libraries.
<figure><img src="image_4.png" /></figure>

image.sc

<figure><img src="image_5.png" /></figure>

Community Partners

<figure><img src="image_6.png" /><figcaption>**Fig. 1.** Screenshot of the image.sc forum in April 2022 showing the logos of the community partners and related communities. Listed open-source projects provide online support for their software on this platform, which might be a key criterion when deciding which software to use.</figcaption></figure>

**MATPLOTLIB** [48] is a scientific plotting and image visualisation collection commonly used for image data science by the PYTHON community.

**MICROMANAGER** [49] is a microscope control software with built-in image processing capabilities based on **IMAGEJ**. It can be scripted using the BeanShell language and recently using **PYTHON** [50].

**MICROSCOPY IMAGE BROWSER** [51] is a MATLAB-based software for advanced image processing, segmentation, quantification, and visualisation of multi-dimensional light and electron microscopy datasets. It works with **BIOFORMATS**, allows batch processing operations and can be directly linked to **FIJI**.

**MORPHOGRAPHX** [52] is a software for visualisation and analysis of 4D datasets. It focuses on the analysis of organ growth from 4D live-imaging confocal data of plants. Various algorithms implemented in **MORPHOGRAPHX** extract surfaces from 3D data and post-process the intensities along those surfaces, which can be seen as an efficient 2.5 dimensional approximation of 3D quantification.

**MORPHOLIBJ** [53] is a collection of methods and plugins for **IMAGEJ** implementing mathematical morphology operations such as dilation, opening, watershed and

reconstruction as well as methods for quantitative analysis of label images.

**NANOJ** [54] is a toolbox of **IMAGEJ** plugins for super-resolution microscopy processing and analysis tasks, including drift correction and channel registration. It also incorporates the widely used SRRF method for live-cell super-resolution image reconstruction [55].

**NEURONJ** [56] is an **IMAGEJ** plugin for neurite tracing and analysis.

**NIS-ELEMENTS** (Nikon, Tokyo, Japan) is a software for microscope control, computer-assisted image acquisition and analysis. It integrates artificial intelligence solutions for de-blurring, segmentation and image restoration. Image analysis components can be combined to a workflow within a visual programming environment.

**NUMPY** [57] is a **PYTHON** library and a collection of efficient array processing algorithms. It is among the most used **PYTHON** libraries in the world [58] and the basis for many image processing components and collections in the **PYTHON** ecosystem.

**PARAVIEW** [59] is a software for vector and surface data analysis and visualisation based on the **ITK** library.
PYTHON is a programming language, potentially the most popular language in science and surely among the top used programming languages in general [44]. It is commonly used to assemble various image processing, data analysis and visualisation libraries in scientific workflow.

ORIENTATIONJ [60] is an IMAGEJ plugin to characterise the orientation and isotropy properties of regions of interest in images.

OMERO [61] is a research data management solution for microscopy image data. It was initially developed to facilitate analysis of large amounts of high-throughput imaging data. OMERO can be used as a remote-server storing image data that is highly interoperable with other software such as CELLPROFILER, FIJI and QUPATH.

OPENCV [62] (open computer vision) is a collection of image analysis components that includes several hundred computer vision algorithms. OPENCV focuses on 2D+time imaging data acquired with video cameras and has also many applications in microscopy.

QUPATH [63] is an image analysis software for quantitative pathology. It allows visualisation and analysis of large 2D slide scanner imaging data of histological slices. Its user-friendly GUI offers tools for manual annotation, machine-learning based tissue classification and deep-learning based cell segmentation. It is extensible using JAVA-based plugins and scriptable using the GROOVY programming language. It is interoperable with OMERO and BIOFORMATS.

R [64] is a programming language for statistical computing and plotting. It is commonly used for the downstream statistical analysis of the output of image analysis packages. R-packages also exist for image processing [65].

RELION [66], or REgularised Likelihood OptimisationN, is a software package for cryo-EM structure determination processing data from single particle or tomography experiments.

RSTUDIO [64] is a standalone application allowing interactive programming using the R language. Users can view existing variables, manipulate tables and plots.

SCIJAVA [67] is a collection of image analysis data structures and algorithms such as IMGLIB2 and serves as the basis for IMAGEJ2.

SCIKIT-IMAGE [68] is a general purpose collection of scientific image analysis algorithms based on NUMPY and SCIPY. Image analysis workflows using scikit-image can be written in PYTHON and it is commonly used with JUPYTER NOTEBOOKS.

SCIKIT-LEARN [69] is a collection of PYTHON-based algorithms for machine learning commonly used in the

context of image for pixel, object and image classification.

SCILS (Bruker, Billerica, MA, USA) is a software for analysis of mass-spectrometry imaging (MSI) data, including machine learning algorithms and tools for visualising ion images and mass spectra.

SCIPY [68,70] is a collection of algorithms for scientific data processing, simulation, optimisation and analysis. It serves as the basis for other software such as SCIKIT-IMAGE.

SERIALEM [71] is an acquisition software for a variety of transmission electron microscopes. It provides different means of automation through navigation, a built-in scripting language and PYTHON integration. Typical applications are electron tomography, large areas for 3-D volume imaging from serial sections or single-particle cryoEM.

SINGLE NEURITE TRACER [72] is a FIJI plugin for processing three-dimensional, multi-channel, timelapse data to trace neurites including analysis and plotting.

SMAP [73] is a MATLAB-based framework for 2D and 3D single-molecule localisation microscopy analysis encompassing tasks such as molecule localisation, image rendering and quantitative analysis.

SR-TESSLER [74] is a standalone software for quantitative analysis of localisation-based super-resolution microscopy data.

STACKREG [75] is an IMAGEJ plugin for 2D + time image registration. It is also commonly used for other types of image registration, for example, for alignment of slices in 3D image stacks.

STARDIST [76,77] is a deep-learning based PYTHON library for segmenting star-shaped objects such as cell nuclei which is also available as plugins for CELLPROFILER, FIJI and QUPATH.

THUNDERSTORM [78] is an IMAGEJ plugin for automated processing, analysis and visualisation of data acquired by single-molecule localisation microscopy. THUNDERSTORM is at the moment of writing not actively maintained and may in the future be replaced by other solutions.

TOMOPY [79] is an open-source PYTHON package for processing tomography data and image reconstruction. It is mainly used for X-Ray tomography.

TOMVIZ [80] is a software package tailored for processing, visualisation, and analysis of 3D tomographic data acquired with transmission electron microscopy. It is compatible with PYTHON scripting to accommodate custom algorithms.

TRACKMATE [81] is a FIJI plugin for object tracking in 2D+t and 3D+t image data. It comes with advanced plotting, track visualisation and cell lineage tree visualisation tools. It is extensible using Java-based plugins
and scriptable using advanced scripting languages in
FIJI such as Groovy, JavaScript and Jython.

**TRAINABLE WEKA SEGMENTATION** [82] is a user-friendly
**IMAGEJ** plugin for pixel classification using various
machine learning techniques based on the Waikato
Environment for Knowledge Analysis [83].

**TRAKEM2** [82,84] is a **FIJI** plugin for registration,
stitching and management of large-scale electron
microscopy data which offers tools for segmentation
and reconstruction of objects such as neurons in 3D.

**ZEN** (Zeiss AG, Oberkochen, Germany) is a software
and collection of components for microscope control,
image acquisition, visualisation and analysis. Its inte-
gration of image analysis and microscope control
allows feedback microscopy applications.

## Emerging software

Apart from our conservative view on the field, we also
perceive recent software developments which presum-
ably will become part of the above glossary within the
next 5–10 years. Most prominently, deep learning
approaches are flooding our field with promising image
processing components especially for image restoration
[85-87] and cell/nuclei segmentation [21,76,77,88], classi-
fication and tracking [89] within complex scenes. Read-
ers interested in an extended list of new applications and
ready-to-use software [2,3] are referred to [90] and the
bioimage model zoo online repository [91]. Those deep-
learning-based components rely on technical frame-
works such as TensorFlow [92] and PyTorch [93] which
are not directly accessible to end-users. Multiple user-
friendly GUIs were recently developed offering modern
deep-learning tools to a wide target audience [94-97].
User-friendly deep-learning-based image processing is
also already integrated within some of the applications
listed in the glossary, namely ILASTIK, MICROSCOPY IMAGE
BROWSER, almost all of the listed commercial software
packages. New commercial solutions, such as AIVIA
(Leica Microsystems GmbH) and APEER (Zeiss AG),
focusing on machine learning for microscopy image
analysis are arising as well.

In the same context, the NAPARI project [98] is bridg-
ing the PYTHON community towards the life scientists
community by offering automatically generated, user-
friendly GUIs to the most recent deep-learning and
data science components and strives to become a
major framework of the bio-image analysis commu-
nity. From a PYTHON community perspective, napari is
already a game changer as it brings widely usable n-
dimensional viewing to the otherwise scripting centred
PYTHON community [99]. From a wider perspective,
more image visualisation tools have been published

recently and show high potential to become major
players within the next decade since, compared to cur-
rent default solutions, they provide opportunities to
processing big image data and applying deep learning
to microscopy image data [100-102].

Processing big image data, in the form of large 3D
image stacks, long 3D+time data or large collections
of 2D or 3D image data sets, is also a hot topic where
new tools developed using remote-data, remote-
computing and network-based approaches are emerg-
ing [103-109] and also semi-commercial solutions are
appearing such as APEER (Zeiss AG). Graphics-
Processing-Unit (GPU)-accelerated classical image pro-
cessing [110-113] will play a major role for overcoming
current limitations concerning processing times for
large image data. From our perspective, such big-data
capable solutions will also facilitate analysing spatial
relationships in biological specimen and tissues using
modern data-science approaches in the context of
spatial-omics and transcriptomics [114-119].

For single molecule localisation microscopy analysis,
improved methods for molecule detection and localisa-
tion are in active development [120]. In the field of
super-resolution microscopy more broadly, there is
also a focus on developing user-friendly methods for
ensuring the fidelity of reconstructed images [121-123].

Last but not least, new file formats and solutions
for research image data management [124-126] are
under development and we expect those to have a
huge impact on how analysts handle image data within
the next decade.

## Aspects to consider when choosing
bio-image analysis software for your
research

The choice of the right bio-image analysis software is
closely related to the purpose of a given research pro-
ject and more broadly to the field a research group is
working in. We suggest becoming confident in a single
software that broadly fits the planned research needs
instead of switching the used software from project to
project just because a single feature may be more
accessible or more accurate in another software. Get-
ting to know software and maintaining expertise comes
at a high cost when numerous potentially incompatible
platforms are used.

Interoperability between software is another key fea-
ture to consider. For example, we discourage using
software that comes with proprietary custom file for-
mats and suggest using broadly available file formats
instead. Most prominently, there are some software
packages with custom formats for project files. We
recommend making sure these project files can be accessed with common text editors and contain human-readable text that is based on standard formats such as XML, JSON, YAML or CSV. It also appears beneficial if software has capabilities for workflow automation or, in the case of plugins, can be automated using the platform they can be integrated with. For example, software with a great built-in segmentation algorithm can become a major bottleneck if the algorithm cannot be integrated with other software, for example, for pre-processing, post-processing, feature-extraction and statistical analysis.

Striving for reproducible bio-image analysis work-
flows with minimal manual interaction steps is key for
analysing large amounts of image data leading to
insights cemented by appropriate measurements
exhibiting statistical power. If the software supports
forming and properly documenting such automated
workflows, reproducibility and interpretability of
results can be ensured [127].

Other technical aspects such as big data capabilities
play a key role, especially when new microscopy tech-
niques potentially producing more and more data are
published every year. Many software packages claim
the ability to work with big data, but often refer to
visualisation only or refer to big data as many images
with a size of megabytes to gigabytes each. On the
other hand, software packages capable of processing
big volumetric image data in the range of terabytes
and petabytes to produce quantitative analysis results
are still rare and often limited in other aspects, for
example, image data dimensionality or imaging modal-
ity. We see more and more web-based solutions being
published diminishing the need to buy expensive com-
putational hardware, to train and execute neural net-
work architectures. When using web-based solutions in
the cloud, institutional, national and international laws
have to be respected. Additional technical burdens hin-
der the wide adoption of cloud computing at the
moment. For example, uploading multiple terabytes of
imaging data from a European institute to an Ameri-
can computing server is not just challenging from a
legal but also from the file transfer bandwidth perspec-
tive. We assume these burdens will fall in the next dec-
ade and the technology will become available to more
and more researchers as the benefits of using it out-
weigh the risks. Hence, choosing a software that is
interoperable with cloud-computing and cloud-storage
technologies appears a future-proof approach.

Many image analysis tasks have a substantial num-
ber of solutions that have been developed, and it can
often be unclear which solution is most appropriate
for a user's specific problem. Several image analysis

fields have established benchmarking challenges,
whereby software is applied to exemplar datasets and
performance is automatically and independently
assessed. Such challenges exist for cell tracking
[128,129], cell segmentation [130], electron microscopy
image segmentation [131] and single molecule localisa-
tion [120]. These provide users with a quantitative
comparison of the state-of-the-art, along with test
datasets and guidance for quality reporting.

Community aspects should also be taken into
account when choosing the right image analysis soft-
ware. A key role in bio-image analysis for microscopy
is played by the Image Science community https://
image.sc, an online forum where developers and users
of most software listed in the glossary are actively sup-
porting each other by providing support and feedback
(Fig. 1). Before using a software mid--/long-term,
users may want to explore this forum and other online
platforms to figure out how actively supported the
software is by a broader community. Furthermore,
some software communities hold regular virtual com-
munity meetings, where users and plugin-developers
can get in touch with core-developers to exchange
ideas, use-cases and receive support. The weekly com-
munity meeting of the napari community and the open
office hours of the CellProfiler community shall be
highlighted here as well-appreciated examples. For
staying up-to-date with new developments in bio-
image analysis software, following new media channels
such as the NEUBIAS Academy YouTube channel
[132] and the @Talk_BioImg Twitter bot which
retweets posts containing the #BioImageAnalysis hash-
tag should be considered as well for an audience with
general interest in the field, for example, postdocs and
core-facility staff working on applied bio-image analy-
sis and image data science. In addition, the Global
Bioimaging infrastructure is organising image analysis
courses and providing a training resource for core
facility staff and image analysis community.

From a group leader's and an institutional decision
maker's perspective, guiding scientists towards using a
common software platform makes sense. The more
local collaborators work with the same software, and
maybe just use different plugins, the more they can
support each other and exchange knowledge. If it is
apparent that a majority of the group or institute
members are using the same software, an institute can
strengthen this community by inviting the developers
of that software annually for courses and seminars.
Building this bridge between users and developers is of
mutual benefit: While the users receive support and
training from the experts, the experts can establish col-
laborations with power users leading to scientific
publications. These applied-science publications are
key to grant applications and sustainable maintenance
of research software.

Conclusions and perspectives

Comparing the bio-image analysis software universe one decade ago to its current state clearly shows that it is expanding. Multiple huge ecosystems grew from a small number of general-purpose software developed more than a decade ago. Recently, many of such platforms are building bridges among them, which is appreciated from our bio-image analysis workflow designers’ perspective. Hence, users are well advised to use interoperable, established, sustainably maintained software packages for their research. It is also important to stay alert on recent developments, especially in the deep learning context, even though one should stay a brave scientist and question those new methods. Such critical thinking is necessary to solidify knowledge about new methods before eventually adopting them as community standards. Last but not least we want to emphasise that the decision on which software to use for specific research projects should be made in groups. Getting in touch with local, regional and global experts and discussing advantages and disadvantages is the right path in a growing universe of bio-image analysis software.

Acknowledgements

We would like to thank Mafalda Sousa (I3S, Porto), Ignacio Arganda-Carreras (Universidad del Pais Vasco, Donostia-San Sebastian), Martin Jones (The Francis Crick Institute, London) for reviewing the manuscript and providing constructive feedback prior to submission. We also would like to thank Michael Nelson (UW Madison), Sebastian Rhode (Zeiss), Ralph Palmisano (OICE Erlangen), Johannes Hohlbein (Wageningen UR), Kirti Prakash (ICR London) and Luciano Guerreiro Lucas (Leica Microsystems) for their constructive feedback about the preprint which we incorporated. RH acknowledges support by the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy - EXC2068 - Cluster of Excellence Physics of Life of TU Dresden. EF, IB and EJ acknowledge support by Biomedicum Imaging Unit and Electron Microscopy Unit, Helsinki University, as a part of Biocenter Finland infrastructure. CT acknowledges support by grant number 2020-225265 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. SC acknowledges support from a Royal Society University

Research Fellowship. AK was funded via the BioI-
image Informatics Facility, a unit of the National
Bioinformatics Infrastructure Sweden NBIS, with
funding from SciLifeLab, National Microscopy Infras-
tructure NMI (VR-RFI 2019-00217), and the Chan-
Zuckerberg Initiative. Open Access funding enabled
and organized by Projekt DEAL.

Data accessibility

Supplementary Table 1 contains all data referred to
this document.

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**Supporting information**

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1.** List of the software terms from the glossary with categorical information and interactive filtering capabilities.