The future of bioimage analysis

Advanced bioimage analysis tools are poised to disrupt the way in which microscopy images are acquired and analyzed. This Focus issue shares the hopes and opinions of experts on the near and distant future of image analysis.

The field of bioimage analysis is poised for a major transformation, owing to advancements in imaging technologies and artificial intelligence. The emergence of multimodal foundation models — which are akin to large language models (such as ChatGPT) but are capable of comprehending and processing biological images — holds great potential for ushering in a revolutionary era in bioimage analysis.

In the ever-evolving landscape of biological imaging technology, it is crucial to develop foundation models capable of adapting to various imaging modalities and tackling complex segmentation tasks.

Concurrent advances in imaging technologies and deep learning have transformed the nature and scale of data that can now be collected with imaging. Here we discuss the progress that has been made and outline potential research directions at the intersection of deep learning and imaging-based measurements of living systems.

Advanced imaging techniques provide holistic observations of complicated biological phenomena across multiple scales while posing great challenges to data analysis. We summarize recent advances and trends in bioimage analysis, discuss current challenges toward better applicability, and envisage new possibilities.

We dream of a future where light microscopes have new capabilities: language-guided image acquisition, automatic image analysis based on extensive prior training from biologist experts, and language-guided image analysis for custom analyses. Most capabilities have reached the proof-of-principle stage, but implementation would be accelerated by efforts to gather appropriate training sets and make user-friendly interfaces.

I share my opinions on the benefits of and bottlenecks for hyperspectral and time-resolved imaging. I also discuss current and future perspectives for analyzing these types of data using the phasor approach.

A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years.

The bridging of domains such as deep learning-driven image analysis and biology brings exciting promises of previously impossible discoveries as well as perils of misinterpretation and misapplication. We encourage continual communication between method developers and application scientists that emphases likely pitfalls and provides validation tools in conjunction with new techniques.

The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR (findable, accessible, interoperable and reusable) data sharing and deliver usable and reproducible analytical tools.

The language used by microscopists who wish to find and measure objects in an image often differs in critical ways from that used by computer scientists who create tools to help them do this, making communication hard across disciplines. This work proposes a set of standardized questions that can guide analyses and shows how it can improve the future of bioimage analysis as a whole by making image analysis workflows and tools more FAIR (findable, accessible, interoperable and reusable).

Here we discuss the prospects of bioimage analysis in the context of the African research landscape as well as challenges faced in the development of bioimage analysis in countries on the continent. We also speculate about potential approaches and areas of focus to overcome these challenges and thus build the communities, infrastructure and initiatives that are required to grow image analysis in African research.

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.