User Experience Research in the Imaging Field

What We Learned by Listening to Researchers, Imaging Scientists, and Bioimage Analysts

CZI’s imaging team wants to help all researchers measure, visualize, and quantify the biological processes underlying health and disease. To reach this goal, we work closely with the scientific community to understand what holds them back and collaborate to improve current systems. CZI supports imaging scientists that increase collaboration between biologists and technology experts, software fellows that maintain critical software tools, biologists who use imaging, and disruptive imaging technologies that will improve our mechanistic understanding of health and disease.

We heard from the scientific community about challenges using bioimaging software — tools that help researchers draw insights from the images their microscopes capture. As we considered what solutions might be possible, we wanted to better understand the experiences of the bioimaging user community: How do these individuals work together and how do their perspectives align? How might we build software for their short-term needs and long-term goals?

By focusing on people first, we aim to stay close to the challenges facing the imaging field and work towards enduring human-centered solutions.

With the help of CZI’s user experience (UX) research team, we interviewed 34 members of the imaging community using the UX research method called persona mapping. Personas are representative profiles of users that help product teams distinguish between different groups, build shared language when talking about groups, and frame understanding of the challenges with a user-centric (rather than product-centric) lens. Before jumping to “should we build feature X or Y,” we want to get to know the researchers who might benefit from such features. When interviewing individuals, we looked for common trends that sparked actionable insights that will keep our users’ needs front and center as we move forward with our strategy.

Imaging Community Personas: Who We’re Building For

Image for post
Image for post
While people in real life exist on a spectrum of behaviors, personas seek to describe distinct differences between groups. Within the imaging ecosystem, our personas have varying levels of expertise across several dimensions.

👩🏽‍🔬 Research Biologist Trainee: Uses Imaging Tools
Research Biologist Trainees are early-to-mid career academics working in a research laboratory. They design, implement, and analyze experiments to test hypotheses about biological questions under the mentorship of a lab’s Principal Investigator. Research Biologist Trainees implement the scientific method and advance understanding of biological processes by sharing their experimental findings through journals and conferences.

👩🏼‍💼 Imaging Core Scientist: Teaches Imaging Tools
Imaging Core Scientists are imaging lifecycle specialists, code-dabblers, and lovers of microscopes who work in an imaging core facility. They train research biologists in the end-to-end imaging process and manage shared facility equipment. Imaging Core Scientists train researchers to be more efficient and accurate imaging practitioners by offering centralized resources and education on the latest imaging techniques.

👨🏿‍💻 Community Bioimage Analyst: Builds Imaging Tools
Community Bioimage Analysts are computational experts that work at the intersection of biology and mathematics. They build machine learning methods and package them as workflows that others can use for their image analysis. Community Bioimage Analysts harness modern analytics for scientific discovery by making their work as accessible as possible to the largest number of researchers.

From our interviews, we found that each group has different goals, needs, and challenges to overcome. Here are four key learnings:

1. Research Biologist Trainees are stymied by the limitations of existing bioimage analysis platforms, relying on communities of practice to discover, learn, and troubleshoot tools

To achieve the level of required analysis, Research Biologists often have to use multiple platforms to perform a single experiment and hunt for specific algorithmic methods via several social networks. Research Biologists acknowledge that each platform has drawbacks, and the absence of a curated algorithm ecosystem makes finding a trusted solution cumbersome, especially when they look to apply a certain tool — such as a machine-learning algorithm to segment cells — only to discover it is hard to install or doesn’t accurately fit their experiment. Biologists yearn for centralized, standardized, and intuitive ways to discover, evaluate, and implement bioimage analysis on large datasets — something our team endeavors to build.

Image for post
Image for post
Multi-fiber streamline tractography showing the white matter fiber tracts in the human brain, extracted using MRtrix software (coronal view). The colors represent the orientation of the streamlines. | Photo provided by CZI Imaging Scientist Pramod Pisharady of the University of Minnesota.

2. Imaging Core Scientists and Community Bioimage Analysts encounter difficulty understanding and meeting the needs of Research Biologist Trainees

Image for post
Image for post
CZI Imaging Scientist Dr. Katarzyna Kedziora of the University of North Carolina at Chapel Hill provides support for image analysis to the UNC community of biological and biomedical researchers and their network of collaborators.

Bioimage Analysts expressed that they often “don’t speak the same language” as their biologist collaborators. One participant noted that the term “segmentation,” a common imaging task for biologists, means something quite different to a software engineer. Another analyst asks biologist colleagues to read through documentation before release in order to flag anything that might not make sense to a non-programmer. Bioimage Analysts struggle to build tools that are user-friendly; they acknowledge user interface design is not their expertise (or interest for that matter; they’d prefer to focus on complex computational algorithms over software interface and packaging!). Both of these challenges impede users’ goals and remind us how interdependent roles are within this ecosystem.

“If you write the most amazing code and nobody knows how to use it, what was the point?” — Bioimage Analyst

3. Everyone has long-term goals to level up their computational abilities

“As people become more advanced, not only will they be able to use more advanced tools; you’re going to inspire more people to come up with their own advanced tools.” — Imaging Core Scientist

Our other personas are also leveling up computational abilities. Imaging Core Scientists want to automate tasks that their facility users repeatedly request, and Bioimage Analysts contribute to low-level algorithmic code because a more mature programming ecosystem makes it easier for them to build plugin tools at scale. Unfortunately, these long-term goals are impeded by the challenges our users face with their immediate needs to use, teach, and build tools.

Part of a whole-slide image of a sentinel lymph node visualized in napari. Data from https://camelyon16.grand-challenge.org/Data/.

4. Great progress has been made, with expectations of more to come

“I’ve seen improvements from incredibly tedious annotations of 10,000 images to a tool so good anyone could use it…I assume it will continue this way. Most of these problems can be solved; someone just has to focus on them.” — Research Biologist Trainee

There is excitement for what machine and deep learning can do for the industry, and a willingness to adopt new tools, as long as the tools don’t sacrifice accuracy, transparency, and bias data. For new technology to succeed, users need to trust the process behind it, easily understand how to find and use it, and adapt it to their specific experiment or tool-building specifications.

What’s Next

Image for post
Image for post
Summary of user needs, which capture what personas want to achieve (not how), and by doing so, avoids narrowing into a feature or a solution too early.

These findings provide a snapshot of how people are working right now, and we plan to continue learning and building alongside users to help drive imaging solutions. Our team has begun supporting the work of napari, a multi-dimensional image viewer platform for Python, and started an initiative focused on the developer services needed to build robust tools. You can learn more about developer services strategies at napari.dev. We know this is just the beginning, and we look forward to sharing progress with the imaging community, fostering transparency and engagement along the way.

To learn more about our work in science and to stay updated on funding opportunities, visit our website and sign up for our mailing list. You can also follow us on Twitter.

More Information

Lucy Obus, Senior User Experience Researcher for Science

Written by

Supporting the science and technology that will make it possible to cure, prevent, or manage all diseases by the end of the century.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store