Background

With constant and exciting advances in microscopy, biomedical and life scientists can acquire images of increasing size and complexity. Thus, manual processing has become limiting, and the use of computational methods allows the extraction of quantitative information from these datasets in an accurate and high-throughput manner.

Automated image analysis is key to extract quantitative information from microscopy images. However, an important limitation faced currently in this time of transition, is that the methods of image analysis developed and used across labs, can be complex to apply and relatively difficult to describe using written protocols. This threatens their reproducibility and calls for sharing the methods used to produce scientific data from images directly as software implementations (workflows). On top of this, most published image analysis methods are not easily searchable and test images as well as the parameters used at every step of a workflow are often not provided, making it difficult to understand the baseline for valid results.

One solution that has arisen to address this issue, is a movement towards making      scientific datasets publicly available, through open data initiatives and web-based software. While an improvement, this still does not represent a unified platform for image analysis which allows access to, and comparison of bioimage analysis (BIA) workflows.

In an aim to tackle this issue, the work by Rubens et al (1) (COST Action CA15124) presents BIAFLOWS (Figure 1), a web-based framework to unify, make available, and benchmark automated image analysis workflows.

Key findings and developments

General developments: BIAFLOWS

• Within the Network of European Bioimage Analysis (NEUBIAS), an important body of work focuses on ensuring a better access to and assessment of existing BIA software. BIAFLOWS is presented as a web-based platform to benchmark bioimage analysis workflows on publicly shared annotated multidimensional imaging data. That is, to assess the accuracy of the workflows by comparing them to a ground truth reference (e.g. human annotation) and quantifying the discrepancy by computing relevant performance metrics. The aim is:
  • To help integrate and compare image analysis methods, hence enforcing highest quality standards, and stimulating the continuous improvements of BIA methods.
  • To offer a complete framework that allows a) handling multidimensional annotated images, b) integrating BIA workflows (standalone or running on BIA platforms), c) remotely visualizing images and workflow results and d) assessing workflows performance from widely accepted performance metrics.

• All content including images, workflows, ground-truth annotations, workflow results  and benchmark metrics, can be browsed and interactively explored through the web interface.
• An online instance of BIAFLOWS is available from this URL:  https://biaflows.neubias.org/ (user: guest, password: guest)
• BIAFLOWS can also be installed locally as a local image management and image analysis platform and all content can be easily migrated between existing instances.

 

Specific features for users and developers

• BIAFLOWS is based on Cytomine (2), an open source web platform developed for the collaborative annotation of high resolution biomedical images.
• The features that were extended or developed for BIAFLOWS are:
  • Support for the upload of microscopy multidimensional images in OME-TIFF format, as well as their remote visualization in a viewer enabling navigation through image slices, adjusting contrast and toggling annotation layers.
  • Support for the remote execution of BIA workflows by encapsulating the workflows and their original software environment in Docker images.
  • Monitoring trusted spaces to automatically pull new or updated workflows      whenever a new release is triggered from their associated source code repository.
  • Version the workflows and make them permanently accessible from the system.
  • Make the workflows ready for any type of computational resources (including high performance computing).
  • Migration tools to transfer content between existing BIAFLOWS instances.

• BIAFLOWS is versatile: the workflows currently integrated to the online instance consist of a mixture of standalone software and scripts (ImageJ/Fiji, ICY, CellProfiler, Vaa3D, Ilastik, Python and Jupyter notebooks).
• Interested developers can package their workflows and make them available for benchmarking from https://biaflows.neubias.org/.
• BIAFLOWS workflow  can also be used to process local images on a machine running Docker, independently of any BIAFLOW server.

 What I like about this paper

I first heard of BIAFLOWS at the NEUBIAS (COST Action CA15124) Image analysis training schools in Porto, Portugal, when its concept and details were presented by author Sébastien Tosi. It caught my attention immediately, as it represents a much expected tool for users and developers of bioimage analysis workflows. While Sébastien Tosi discussed the current challenges of sharing BIA workflows, I could identify all very well: through my career, it has been difficult to search for, find, share, compare, and validate workflows. Equally, most imaging datasets from published work, are hardly available. A tool that compiles the many components necessary for BIA workflows is a huge step forward into openness and reproducibility into the important step of image analysis- an ever growing field of increasing importance.

Open questions

• Note: all questions with answers from authors are at the bottom of this page.

1. The idea of BIAFLOWS is exciting. How did you come to create this, and how do you see it developing further as the fields grow (both of bioimage analysis and of microscopy)?
2. Under the lines of the previous question- the creators of several open source tools offer training to involve and benefit as many scientists as possible. For those scientists who are new to imaging and image analysis (eg young PhD students, or scientists later in their careers beginning in either microscopy or image analysis) how will you tailor this excellent tool so that users at all levels of knowledge can benefit from it?
3. How do you envisage integrating BIAFLOWS in the publication process? For instance, at present, most journals do not require depositing original imaging datasets from which quantifications or analyses are presented. In the past this was the case for bioinformatics analyses until open distribution/access became a norm. Do you envisage that BIAFLOWS could help make this possible? A repository of images that helps reproducibility and transparency in every way?
4. In your paper you mention that in many cases of image analysis, imaging datasets from microscopy are a minority compared to medical imaging. In your opinion, what makes this difference between medical and biomedical-oriented imaging?
5. You specify the features that BIAFLOWS currently possesses. We know that constantly, imaging methods, microscopes, and image analysis tools are being developed. With such rate, what is the workforce that will keep BIAFLOWS updated, and the results of comparisons of different workflows published and available? And do you see a need for increased funding and developers’ involvement worldwide to integrate into this effort?

 

References

1. Rubens et al (2019). BIAFLOWS: A collaborative network framework to benchmark bioimage analysis workflows, bioRxiv, doi: 10.1101/707489
2. Maree et al (2016). Collaborative analysis of multi-gigapixel imaging data with Cytomine, Bioinformatics, 32 (9): 1395-401.

 

Acknowledgements

I thank Sébastien Tosi and Raphael Maree for their time discussing their work, and their input on this highlight. I also thank Mate Palfy for his helpful comments. Finally, for this particular highlight, I thank the NEUBIAS (COST Action CA15124) Image analysis training school in Porto, Portugal, which allowed me to become acquainted with this interesting and promising work in the first place.

Tags: image analysis, imaging, microscopy, workflows

Posted on: 29 November 2019 , updated on: 30 November 2019

doi: https://doi.org/10.1242/prelights.15323

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