ISSN 2186-7437

NII Shonan Meeting Report

No.167

Formalizing Biological and Medical
Visualization
Renata Georgia Raidou
Barbora Kozlı́ková
Johanna Beyer
Timo Ropinski
Issei Fujishiro
February 24–27, 2022

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Formalizing Biological and Medical
Visualization
Organizers:
Renata Georgia Raidou (TU Wien, Austria)
Barbora Kozlı́ková (Masaryk University, Czech Republic)
Johanna Beyer (Harvard University, USA)
Timo Ropinski (Ulm University, Germany)
Issei Fujishiro (Keio University, Japan)
February 24–27, 2022

Abstract
Medicine and biology are among the most important research fields, having asignificant impact on humans and their health. For decades, these fields havebeen highly dependent on visualization—establishing a tight coupling whichis crucial for the development of visualization techniques, designed exclusively
for the disciplines of medicine and biology. These visualization techniques canbe generalized by the term Biological and Medical Visualization—for short,
BioMedical Visualization. BioMedical Visualization is not only an enabler for
medical diagnosis and treatment, but also an influential component of today’s life
science research. Many BioMedical domains can now be studied at various scales
and dimensions, with different imaging modalities and simulations, and for avariety of purposes. Accordingly, BioMedical Visualization has also innumerable
contributions in industrial applications. However, despite its proven scientificmaturity and societal value, BioMedical Visualization is often treated within
Computer Science as a mere application subdomain of the broader field ofVisualization.
To enable BioMedical Visualization to further thrive, it is important toformalize its characteristics independently from the general field of Visualization.
Also, several lessons learnt within the context of BioMedical Visualization may
be applicable and extensible to other application domains or to the parent fieldof Visualization. Formalization has become particularly urgent, with the latest
advances of BioMedical Visualization—in particular, with respect to dealing withBig Data Visualization, e.g., for the visualization of multi-scale, multi-modal,
cohort, or computational biology data. Rapid changes and new opportunities
in the field, also regarding the incorporation of Artificial Intelligence with“human-in-the-loop” concepts within the field of Visual Analytics, compel further
this formalization. By enabling the BioMedical Visualization community tohave intensive discussions on the systematization of current knowledge, we canadequately prepare ourselves for future prospects and challenges, while also
contributing to the broader Visualization community.
1During this 4-day seminar, which was the 150th NII Shonan meeting to beorganized, we brought together 25 visualization experts from diverse institutions,backgrounds and expertise to discuss, identify, formalize, and document the
specifics of our field. This has been a great opportunity to cover a range of
relevant and contemporary topics, and as a systematic effort towards establishing
better fundaments for the field and towards determining novel future challenges.
In the upcoming sections of this report, we summarize the content of invited
talks and of the eight main topics that were discussed within the working groups
during the seminar.

2

Executive Summary
The field of BioMedical Visualization is a living system, which receives input
from its environment–in the form of new challenges. For example, it has to deal
with new imaging modalities, or it has to address new applications, and it has toincorporate new technology. When receiving all this energy-rich input, as shownin Figure 1, the system becomes increasingly chaotic, oscillating between different
states. At one critical point, the system will either break apart (disintegration)or it will leap to a higher, more complex order (sustainability)—now, able tohandle even more challenges.

critical point
creative dynamic
sustainability
innovation
“art”
chaos
fluid
flexibleenergy input

new imaging
simulations
new techbig complex data
new applications
human-inthe-loop

stagnation rules
rigidcertainty limits
controlguidance

disintegration

Figure 1: The field of BioMedical Visualization is a living system, which constantly addresses new challenges.
At this critical point, we have two dimensions, as shown in Figure 2. The first
dimension relates to the incorporation of fresh ideas, innovation, new challenges
and so forth. The second dimension relates to the establishement of rules, ofa clear vision of the future and of a form of control. A balance between thesetwo dimensions must be kept at all times. For example, if we have too muchdynamics but no clear vision, the research field faces the risk of incoherence. On
the other side, if we have too many rules and no innovation, the research field
becomes rigid and inflexible. Both states lead to disintegration, i.e., breaking
apart, of the living research field. Keeping the right balance for sustainability iswhat can help our field to move up and to thrive.
The main aim of the seminar was to formalize the current status of the
field of BioMedical Visualization and to determine future challenges for novice
researchers. While tasks and requirements in BioMedical Visualization differ fromthe general field of Visualization, these differences have not yet been systematizedindependently. A free and open discussion between all participants has beensought—opening novel, interesting paths for the future and contributing to new,creative collaborations. This effort might serve in the future as a model forother research fields within the visualization community, where interdisciplinary
collaboration is also required.
3

innovation
fresh ideas
dynamics
challenges…

vision “tradition”
rules
control …

Figure 2: Keeping the right balance between novelty and vision will help thefield of BioMedical Visualization to thrive in a sustainable state.
Before the beginning of the seminar, the organizers identified seven topics,
which they believed were relevant for all involved participants. These were
defined based on the three main pillars around which BioMedical Visualization
revolves: the Data, the involved Stakeholders, and the conducted Tasks andProcesses. On the first day of the seminar, a semi-formal discussion lead to theintegration of two more topics, while the pre-determined topics were reworked(i.e., some were merged and others were split), to form a total of eight topics tobe discussed. These eight topics are schematically depicted in Figure 3.
Differences between BioMedical Visualization and the parent field of Visualization

Where formalization is needed for BioMedical Visualization?
Best Practices in BioMedical Visualization
Integration & Standardization in BioMedical Visualization
Complexity vs. Knowledge in BioMedical Visualization
Reusability, Generalizability, Adoptability in BioMedical Visualization

Curriculum: How can we structure a BioMedical Visualization course?
Dissemination: Driver of Formalization in BioMedical Visualization

Figure 3: The eight topics discussed in the working groups of Seminar n.167 on“Formalizing Biological and Medical Visualization”.
The upcoming sections of this report include abstracts from the invited andpanel talks during the seminar, as well as a summary of the discussions withregard to the aforementioned eight topics.

4Overview of Talks
I. Sustainable Applied Visualization Research
Ingrid Hotz, Linköping University, Sweden
In applied research projects, challenges and expectations are often very different
from those for basic research projects with a clear technical contribution. In
many cases, the anticipated outcome and the measures of impact or success areunclear. In this talk, I did not give answers to these questions but talk about
experiences from three applied research projects with different outcomes. The firstexample is the development of a “Visual Environment for Hypothesis Formation
and Reasoning in Studies with fMRI and Multivariate Clinical Data”. The
second example is based on a collaboration with theoretical chemists performing
molecular dynamics (MD) simulations. The third example is a collaboration
with mechanical engineers dealing with multivariate data from finite element
simulations for the evaluation of the performance of mechanical parts. The
outcome in these projects ranges from software prototypes to a talk given in anindustrial forum, which resulted in the hiring of a visualization expert. While allthese projects are success stories to some extent, they all also leave the question
about sustainability and lasting impact open.

II. BioMedVis—Opportunities & Threats
Helwig Hauser, University of Bergen, Norway
Out of a brief assessment of the state of the art in both biological data visualization and medical data visualization, and based on a short review of current
strengths as well as weaknesses, a number of opportunities are identified anddiscussed, including (1) new data and new forms of data, (2) multi-modal datavisualization, (3) the visualization of physiology, (4) the study of entire cohorts,(5) comparative visualization, especially when more than two datasets are to
be compared, (6) the study of multi-scale phenomena, (7) the study of multidimensional problems, (8) data science in computational medicine, biomedicine,and biology, (9) the integration of modeling and simulation, as well as (10) the
more conscious focus on biomedicine, precision medicine, etc. This discussion ofopportunities comes with a brief recapitalization of associated threats, such as
the risk of redoing too much too often, getting lost in details, the non-availabilityof data, especially in the multi-scale case, the risk of working solution-oriented(instead of problem-oriented), the possibly too extended distance to the domain
and an according lack of interdisciplinary collaboration, the threat of becoming
partially obsolete due to machine learning, and—as “always”—a lack of funding
combined with difficulties associated with the evaluation of interdisciplinary
research proposals.

III. Formalizing Biomedical Visualization: Connecting Macromolecular Structures to the Big Picture
Marc Baaden, CNRS Paris,France
Macromolecular structure visualization is a historic field in BioMedViz and going5

back through a few of its milestones and characteristics may provide usefulclues about formalizing and advancing the larger domain of BioMedViz, aswell. A few particular lessons may be learned about the big divide between thecomputer science and domain communities, about the crucial role of softwareand limitations of current practices, as well as about how to get stakeholders on
board of the latest evolutions in the field. A few community-wide used codes
have been provided for more than 20 years now by a few research groups, many
more specific tools exist with varying degrees of availability. Most conceptual
prototypes however seem to become quickly inaccessible after initial publication.
This situation has consequences on how innovation may—or may not—transferfrom the core visualization research community to the domains of application.

IV. Essence of Data Visualization: Putting the Form inFormalizing
Martin Krzywinski, Canada’s Michael Smith Genome Sciences Center at BC
Cancer, Vancouver, Canada
Well-designed figures can illustrate complex concepts and patterns that may
be difficult to express concisely in words. Figures that are clear, concise andattractive are effective–they form a strong connection with the reader andcommunicate with immediacy. These qualities can be achieved by employing
principles of graphic design, which are based on our understanding of how weperceive, interpret and organize visual information. Additionally, because figuresoften act as a first explanations, it is critical to distinguish essentials from details
and merely interesting tangents–not an easy task for the researcher who feelsthat all their hard-won data should be shown. While everything may indeed beimportant, initially some things are more important than others. Classifying
aspects of the science this way always feels risky—how do I know that I know
enough to justify leaving things out? Using critique by redesign, this talk distills
core concepts of information design into practical guidelines for creating scientificfigures.

V. Panel Discussion: Formalizing Biological and Medical
Visualization
Helwig Hauser (moderator), Stefan Bruckner, Michael Krone
In this panel, we want to stimulate the discussion on the rationale for, feasibilityof, as well as benefits and drawbacks of formalizing the area of biological andmedical visualization by presenting to opposing viewpoints presented by the twopanelists. The viewpoints correspond to extreme positions not necessarily heldby the panelists, but aim to outline the continuum of opinions on the subject.
Stefan Bruckner: I advocate the position that a too strong focus on thesubfield of biological and medical visualization is counterproductive. While thisarea certainly addresses an important set of applications for visualization technology, it does not exhibit fundamental conceptual or methodological distinctions
from other application domains of visualization. By attempting to single out
medical and biological applications, we contribute to a balkanization of the field,
6

leading to reduced exchange and hence potentially limit the impact of our work.
Visualization in the life sciences is “just another application”, albeit a highlyinteresting and important one!
Michael Krone: I took the position that biomedical visualization (or “lifescience visualization”) can be seen as a field of its own and not just anotherapplication area of visualization since there are a couple of distinctive points suchas the privacy issues, restrictions and ethical implications for example related tohealth-related data. The field also has separate conferences and meetings that
focus only on biomedical visualization. Examples are EG VCBM or the BioVis
symposium, which used to be collocated with IEEE VIS but is now a COSI at
ISBM. Biomedical visualization can be compared to bioinformatics or medical
informatics, which are part of computer science but are also independent fieldsof research that require a distinctive set of skills taught in specialized bachelor
and master programs. A similar approach could be possible for biomedical
visualization, which could be a masters program for specializing after getting
a bachelor’s degree in CS or BioInf, since it is not possible to teach the skills
required for biomedical visualization in one course. A proper formalization ofthis field could be a primer for establishing biomedical visualization as a distinct
discipline.

7Topic Summaries of Working Groups
We will now summarize the findings of the respective group discussions. Each of
the challenges lists all participants of the respective session(s) in alphabeticalorder. Each of the participants took part in two groups. Each group had amoderator, whose name is denoted with bold font.

Topic 1—Difference between BioMedical Visualization andthe parent field of Visualization
Johanna Beyer, Helwig Hauser, Tobias Isenberg, Renata Raidou,
Timo Ropinski, Thomas Schultz
Motivation During this seminar, the question “Are we just another application
field within VIS? ” has been raised very often. To formalize the domain of
Biological and Medical Visualization (BioMedVis), we first need to understandthe characteristics of this particular domain, the aspects that unite us as acommunity and the points that differentiate us from the other application fieldsof the general Visualization (VIS) discipline. Our working group identified as thestarting point of the discussion that the currently used term of “Biological andMedical Visualization” is too narrow. This term implies that the focus of our work
is to provide visualization solutions for the medical or biological domain, while
the actual research focus of our community is far broader than this—both withregard to the employed technical methodologies, and the addressed application
domain. A common agreement is that working within the BioMedVis domain
requires to obtain early and profound knowledge of the respective life science ormedical domain, on top of the technical expertise in, e.g., visualization, image
processing, image analysis, machine learning, and so forth. To determine a moreaccurate and broader term that describes our research domain, we also need toconsider the disciplines, the backgrounds, and the personal research interestsof the researchers that are part of our community. In the upcoming sectionsof this report, we initially present the identified challenges that differentiate
BioMedVis from VIS, as well as the aspects that create a common ground for
all researchers working interdisciplinary within the BioMedVis domain. Also, we
present a number of outcomes and actionable points with regard to the identified
challenges.
Challenges Clarifying the difference between BioMedVis, VIS, and adjacentsubfields can help us to better address the following practical challenges:• Education: Does it make sense to offer specific classes on Biomedical
Visualization? How should they relate to a general class on Visualization?
What are the prerequisites for learning about the details of our field? Are
there specific teaching goals, particularly for BioMedVis?
• Community: Which expertise is required to address the major research
problems within Biomedical Visualization? What is the best way to involve
domain experts? From which technical fields other than Visualization do
we need to involve experts? Where do we associate ourselves within the
research community? Given that we have many different backgrounds,
what commonalities do we have with each other?
8• Publication culture: What are the most suitable venues for disseminating
work on Biomedical Visualization? Do we need new conferences or journals,
or new types of publication? What about publishing software and data?
• Funding: What are suitable sources for funding interdisciplinary researchwithin Biomedical Visualization?
• Research questions: How do we view the roles of fundamental vs. domainspecific research questions within our field? How has the SciVis/InfoVis/VA
separation influenced our community?
Outcomes The outcome of the initial part of our discussion regarding theidentification of characteristics and the involved disciplines of the BioMedVis
domain can be summarized with the Venn diagram in Figure 12:

VDS

VIS

BioMedVis

VCBM

Figure 4: Venn diagram representing the position of the domain of BioMedVisin respect to the parent VIS field, the domain of Visual Computing in Biology
and Medicine (VCBM) and the newcomer field of Visual Data Science (VDS).
This diagram illustrates the interdisciplinarity of our research field.
What distinguishes BioMedVis from the general field of visualization is thatit requires knowledge, and collaboration with partners from the life sciences
or medicine. In this respect, it is similar to other application domains invisualization, and it is a regular part of visualization without any fundamental
differences. However, we also view BioMedVis as part of a broader and highlyinterdisciplinary field, Visual Computing in Biology and Medicine (VCBM). This
term does not fully overlap with visualization, but rather includes numerous
sub-fields of computer science, such as image processing or computer vision, as
well as statistical and machine learning techniques, which are applied to visual
data from our application domain, or integrated with visualization. We identified

9

“life sciences” as a term that is more comprehensive, and in our opinion moresuitable to describe the scope of our field, than “biology”. However, we believethat “life sciences” should not replace “biology and medicine”, since it might beseen as excluding techniques that support the practice of medicine, as opposed to
medical science / research. However, we believe that our domain should promote,e.g., in formulations of calls for papers, the term “life sciences” to facilitate theinclusion of new application domains and new practices within our research. We
identified “Visual Data Science” as an emerging term that is increasingly being
used to describe an approach in which data management, machine learning, andstatistics, are tightly integrated with visual data analysis. We expect that its
role within the domain of life sciences and medicine will increase, and believethat the VCBM community should be open to this development.
Concerning the specific challenges listed above, we came to the following conclusions:• Education: We see two main potential target groups for a BioMedVis
education: Students from computer science, and students from the domainsciences. In the former case, the contents should be more technicallyoriented, with a focus on understanding methodology and being able todevelop and implement it oneself. Depending on the local circumstances,the course might be specific to life sciences and medicine, or it might bea more general class about applied visualization. A course that targets
students from the application domain should focus on the correct andeffective use of available techniques.
• Community: We see clear advantages of collocating our events, such as
VCBM, with other meetings, which could be either from an application, oran adjacent technical domain. Due to the diversity of our field, we see nonatural candidate for a regular collocation, but we would welcome interchanging collocations, depending on which opportunities offer themselves.
In addition to that, we should promote the reverse “flow” by supportingwith our participation venues of local domains of expertise, e.g., local
medical or bioinformatics conferences. This could be an excellent sourcefor new ideas and new collaborations with domain experts.
• Format of VCBM: We see VCBM as an opportunity to experiment with
non-standard formats, such as hackathons, breakout sessions, hands-on
workshops, or bring-your-own-data sessions. This would allow us to showcase to domain experts the endless possibilities that our domain offers, andit would help us gain more knowledge about the domain of application.
Additionally, we believe it would increase the visibility of VCBM on the
web if vcbm.org, where a web portal (which could prominently link tothe current VCBM workshop) instead of a forward to the current year’s
event. Such portal could include additional relevant information for new
researchers, and ways to create and promote links within the community.
• Involvement of domain experts: For good reasons, technical and application
communities will keep their separate venues. However, we would like to see
an increased exchange between these communities. As one instrument, we
encourage VCBM organizers to invite presentations from domain experts,

10preferably from their local communities, as well as to participate more
often–when/if possible–to domain venues.
• Publication culture: At this point, we see strong benefits from keeping
VCBM as an opportunity to present also more experimental or morenarrowly focused work. An interesting strategy would be to help domain
experts publish in their venues a showcase of our visualization work.
• Research questions: In our workshop calls and assessment of submissions,
we should be inclusive with respect to currently underrepresented “lifescience” topics, such as biomedicine (which might provide a stronger link
between the currently somewhat separate directions of biological andmedical applications), visualization of abstract data, machine learning.

Topic 2—Where formalization is needed for BioMedical Visualization?
Marc Baaden, Stefan Bruckner, Issei Fujishiro, Helwig Hauser,
Barbora Kozlı́ková, Martin Krzywinski, Johannes Sorger
Motivation The initial aim of this topic was to detect to which degree theformalization of the BioMedical visualization (BioMedVis) is necessary, in which
way and where it would be clearly beneficial, and who could benefit from that.
Based on the discussion, we were trying to give examples of formalization fromother fields and suggest possible future action points in this direction.
Challenges First of all, all participants of the discussion agreed that it isdifficult to define the BioMedVis field from the methodological point of view,and the first part of the discussion was devoted to the subject of finding thecharacterizing lines of such a combined field. Both fields are facing complex
multiscale phenomena and one of the main challenges in both of them is asimultaneous handling of the interplay between different types of data, suchas spatial, multidimensional, nominal, and temporal. This can quickly leadto the problem of maintaining the visual elements – we can run out of colors,
shapes, and other visual variables. Therefore, it can be overwhelming to solvethese problems at the same time. Also, especially in medical visualization, theproblems being solved were originally very spatially-oriented. Nowadays, bothbiological and medical visualization are facing big heterogeneity in data, theyneed to incorporate visual analysis into their solutions, etc.
Further we were also discussing the positioning of the BioMedVis in thecontext of a broader perspective, such as life sciences, or nutrition science. We
pointed out that the definition of the ”first principles” of BioMedVis is missing,stating how to visualize the target dataset. Here we could be inspired by thegeneral principles proposed by Tamara Munzner [9]. In our fields, the nature of
the underlying phenomena, measurements, and multifaceted-nature of the data
plays a crucial role, which needs to be taken into account.
In order to better define the BioMedVis field, we conducted a thought
experiment where we were trying to define the scope of a hypothetical BioMedVisjournal and detect borderline cases for the editor to decide for a desk-reject.
One of them could be that the paper is focusing too much on the inorganic

11chemistry or the lack of sufficient technical contribution from the visualization
perspective. Here we discussed if incorporating only the image processing or
rendering and computer graphics techniques are acceptable contributions andwe came to the conclusion that such papers should still be considered as apotentially beneficial contribution to the BioMedVis field. Generally we agreed
that an acceptable paper should cover biomolecular visualization for medicine,with a strong application for a disease. The scope can be broadened from human
medicine to animals and plants as well. But the scope of such hypotheticaljournal should be also derived from the needs of the community and their tasks.
Therefore, in the following discussion, we were focusing on the better definitionof the target audience and who could potentially benefit from the progress informalizing the BioMedVis field.
Here we detected the following potential beneficiaries of the formalization:• Students and collaborators (domain scientists)– formalizing the field
and introducing the consistent terminology can reduce the ambiguity interms of use of the visual elements that are mapped onto specific meanings.
It can decrease the entry barrier for entering the field and can help with
reducing the situations when the visualization is wrongly perceived. The
collaborators would benefit mainly from finding the common language
between the domains.
• Visualization designers – similar benefits as students, with possible
difference in the source of gaining the information (mostly webpages). Hereit is important to state the domain conventions which should be followedby the designers.
• Visualization researchers – except for the same benefits as for thedesigners, the researchers could additionally benefit from the software
standards, standards for interactions, interoperability, etc.
• General public – can enable to reach better literacy, can lower theburden to understand the conveyed information. In other fields, such as
geovisualization, it was already proved that standardization leads to fasterlearning and understanding.
Based on that, we were trying to discuss how this meeting could influence
the future of our community – shall we separate or rather more integrate withthe general visualization community? This topic was also extensively discussed
in the discussion panel of the meeting. Except for that, we were raising the issueof the lack of presence of the domain experts at the visualization venues, suchas BioVis or VCBM workshops. The reason could be that the need from thedomain scientists is not so strong yet. The individual collaborations are whatmatters the most. Currently there is still a big difference between knowing by
the experts what is possible and what is needed. If there is a huge bottleneck in
the field where visualization could be helpful, the experts will come and searchfor help.
Examples of formalization Another important topic of our discussion was
to compile a list of examples which could inspire us in the formalization process.
These are:12• An algebraic process for visualization design by Kindlmann and Scheidegger [7].
• Goal-oriented taxonomy of visualization, such as the Wehrend’s matrix (Aproblem-oriented classification of visualization techniques by Wehrend andLewis [17]). Similarly, we can be inspired here by the Tamara Munzner’s(action,target) pairs [9].
• Many published attempts to formalization are either too general or too
specific, but can still serve as an inspiration. Here we mentioned again
the Munzner’s book [9], the book on Visual Computing for Medicine:
Theory, Algorithms, and Applications by Preim and Botha [11], or thepaper entitled Visualization and Visual Analysis of Multifaceted Scientific
Data: A Survey, by Kehrer and Hauser [6].
• Improving figures and visualizations to make them more readable is one of
the main challenges. Here we can be inspired, for example, by a paper by
Vuong et al. [16]. Here belongs also the discussion about introducing thebias by presenting the data in different ways. We mentioned a well-known
example, see Figure 5.
• Another crucial aspect is to handle the text in visualizations in a better way.
The standards for labeling should be set, they should take into account,for example, the hierarchy in the data. Symmetry in data should lead also
to symmetry in labeling (positioning the labels, their alignment, ...). The
rules from typography should be applied to visualization.
It is also worth to mention that formalizing BioMedVis field is not just about
defining static visualization, but also the dynamic processes which are often the
crucial part of the visualized phenomena, which need to be interactively exploredas well.
In general, we need to work towards better communication of science. If thevisualization product is useful and is meeting the needs of the collaborators, it
can be considered as successful. Therefore, if we will be able to agree on somerules, this is already very valuable. It will enable to define the interface between
the two communities sharing the visualization – the visualization researchers
and the domain experts, who are in need of more comprehensible and efficient
ways of exploring their data.
Outcomes In this part of the discussion, we aimed to identify potential tangible
outcomes which could be followed in the future. Here we suggested the following
action points:• Invariants of good visualization – when such invariants are defined,
they could be published as a Points of View column in Nature Methods.
We could come with 3 or 4 pieces and in each one of them, we would choose
one invariant to be presented. Here, each of these invariants should beworked out through a selection of examples. We already detected possiblyinteresting invariants – to identify a clear axis of the visualization, to be
conscious about the hierarchy in the visualization, to discuss the color
schemes, typology, or other aspects.

13Figure 5: The effect of the method of data display on physician investigators’decisions, taken from [4].
• Getting visualization to the next level – here we want to propose an
Opinion piece article addressing, for example, the ”graveyard” of software
prototypes and how to improve the current situation in that, the lack ofsupport for searching across images (indexing the image content by keywords), or the standardization agreement based on rules (interoperability,e.g., arcballs). Regarding the indexing system for searching across images,
we can envision a similar solution to the text-based search. Each figure
could be described by 5 keywords, serving for subsequent search.
• Inspiration from the existing guidelines – we can be inspired by thealready established and used guidelines in other fields, such as the IUPAC
guidelines in chemistry (https://iupac.org/).
• Where to further discuss these topics – to continue with the discussions and potentially also include more people, we could propose aworkshop at the VIS conference which could help to initiate the follow-up
discussions and other outcomes regarding the formalization idea.
In conclusion, we agreed that even if we are probably not able to globallyformalize the BioMedVis field, the formalization can be still performed on thelocal level of individual collaborations, which can be of great benefit.

14Topic 3—Best practices in BioMedical Visualization
Michael Krone, Tobias Isenberg, Renata Raidou, Noeska Smit
Motivation This working group was established in order to discuss if there
are any best practices and recommendations that are specific to biological and/or
medical visualization (BioMedVis). Such recommendations could serve as astarting point for graduate students that are new to the field. Furthermore, astructured collection of best practices from these fields could serve as a basis
for the formalization based on previous outstanding examples. Such a literature
survey could also help to identify white spots in the landscape of BioMedVis
research where more work is required.
Challenges In order to identify what best practices are for BioMedVis, we firstneeded to identify whether there is anything specific about BioMedVis researchversus visualization research applied to other fields in general. An issue that
immediately comes to mind when working with patient-specific data. The rules
for protection of such data are more strict, also under the GDPR regulations
since they fall under the personal data provisions 1 . Anonymizing such data
is challenging. Even if a medical imaging scan only has a number assigned toit, as soon as there exists a coupling list between these numbers and patient
information, the data is not anonymous, but rather pseudonymous. In addition,
data like CT scans of the head can feature identifying information even within
the imaging data itself. One approach to work with sensitive data is to keep
the data where it is best protected, e.g., within a secure hospital environment.
The visualization software can then be developed with ‘safe’ data externally anddeployed within the hospital environment for research purposes. For most ethicalconsiderations, the main responsibility lies with the domain experts acquiring
the data. Project partners may need to re-apply for ethical clearance if theywant to re-use the data in other projects. Sometimes, domain partners are noteager to publish findings in visualization papers before they have had the chance
to publish. Alternatives could be to show images of similar data or to present
the results vaguely. Even when there is no direct patient data involved, any
research involving people, as for example in user studies, should be carefully
treated. In biology, there are also protection mechanisms in place related topreservation of rare plant species. Developing a repository of datasets to verify
our techniques (benchmark) could be fruitful, for example something like thelist on medvis.org2 , but at a higher level. A starting point could be data fromprevious VIS challenges.
Another challenge arises in BioMedVis when attempts are made to translate
research software into clinical practice. Strong certification regulations are inplace for any software that influences patient care decisions that need to beobtained. These patient care decisions also lead to a situation where cliniciansneed a high level of trust in visualization solutions before basing any decisions
on them. A question arises also on who is responsible for the outcome of any
such decisions when visualization software was involved in making them.
A challenge that holds for both biology and medicine is that these fields
are dealing with processes that are not easily controllable. These processes are
1 https://gdpr.eu/eu-gdpr-personal-data/
2 https://medvis.org/datasets/15

multi-scale both in space and time, leading to interesting visualization challenges.
Part of the discussion in this breakout group focused on previous papers
that are good examples from which a formalization could be inferred. We firststarted with theoretical considerations of how a good BioMedVis paper should be
structured. As in all visualization papers that are targeting a specific application
domain, sufficient domain background knowledge needs to be included so that a
visualization researcher can follow the paper. However, this section should nottake up too much space in the paper and should not mention unnecessary details
that might be interesting but not needed to understand the paper. Here, it isimportant to mention that on best practice when writing about BioMedVis shouldbe that the text should be tailored to a specific audience. Since BioMedVis often
addresses health-related problems, it is often also interesting to a lay audience.
That is, not only the resulting visualization targets a general audience, but thisgeneral audience might also be interested in the visualization itself, not just thevisualized data. Similarly, a paper could be targeted at domain experts wanting
to work with BioMedVis for exploratory analysis and, consequently, wanting tounderstand it to be able to judge its reliability.
When trying to come up with examples of successful BioMedVis research, we
divided them into examples that had a tangible impact since they are now usedin practice and examples that we think were highly influential on subsequent
research in the field. The following examples fit into the first category:• Many techniques for visualizing the results of medical imaging, like 3D ultrasound rendering, Virtual Colonoscopy, and Curved Planar Reformations [5]are nowadays used in clinical practice for diagnostic tasks. High-quality
volume rendering is also used in touch-screen tables by SECTRA, which
are used in diverse settings, such as medical visualization, virtual autopsy,
and outreach (museums).
• For the visualization of three-dimensional molecular structures, tools likeVMD and UCSF Chimera have been developed and are now routinely used,
for example, by computational chemists and structural biologists for theirresearch. These tools include molecular representations, like MolecularSurfaces.
• Biological network visualization tools like Cytoscape are used for researchin academia as well as industry.
• BioMedVis is also used in practice for educational purposes. One example
for this is the biomolecular visualization tool marion, created by Ivan
Viola’s group at TU Wien, which is now used by the illustrator Drew
Berry to create educational movies about whole cells. Similarly, medical
visualization research at TU Delft has led to the creation of a Massive
Open Online Course (MOOC) for anatomy education3 .
The following papers are examples for highly influential works with respect
to subsequent research in the field of visualization:• Researchers from University of Münster, Germany and Linköping, Sweden
have investigated the influence of different illumination models on image
3 anatomy.tudelft.nl

16

comprehension in direct volume rendering [8]. Their findings have direct
implications for (especially medical) volume rendering.
• For the visualization of molecular structures, Tarini et al. wrote a paper
showcasing the use of ambient occlusion and edge cueing to enhance theperception of these complex, three-dimensional structures in real time [12].
This paper is still inspiring and influencing research in this area.
• Visualization research is usually published in specialized journals thathave a rather low impact factor compared to other disciplines. One early
success story is the seminal work of Connolly on the visualization of smooth
Molecular Surfaces, which was published in Science. The fundamentals
presented in this work are used to date for molecular surface rendering.
Another notable paper that was published in the high-impact journal
Nature presents the visual analysis of mass cytometry data by hierarchicalstochastic neighbour embedding [15].
• BioMedVis tools like VolumeShop [2] or MegaMol are used as prototyping
platforms for further research by PhD students (at least in the respective
groups), leading to shorter development cycles and, consequently, an
increased publication output.
Please note that the examples mentioned above are neither meant to be
an exhaustive list nor necessarily the highest-ranking examples. The list justrepresents examples from both fields what were mentioned during the breakout
discussion by the four participants. A good starting point to identify further
work that could be used to infer best practices for the field are the recipients ofthe biannual Dirk Bartz prize for medical research, as this prize also considers
implications for impact in a clinical setting.
Outcomes The working group discussed recommendations in order to increase
the chances for our BioMedVis to have practical impact:• Put an extra effort to showcase results, methods, and tools beyond publishing the paper. This increases the visibility of the work, increasing thechances of practical uptake. Concrete recommendations here are:– Social media outreach, e.g., Twitter, Facebook, Blogs, etc.
– Making source code available, e.g., Github, permissive licensing, etc.
– Making author copy of paper available, e.g., arXiv, HAL, etc.
– Making video demos and tutorials available, e.g., Youtube, Vimeo,
etc.
– Making a project page/website with, e.g., demo (executable), example
images, videos, data (if possible)– Presenting in application domain venue, e.g., a poster, tutorial, application/tool paper– Presenting at outreach event or exhibition– Publishing a meta/summary/overview paper in venue for larger audience, e.g., CS, popular science, etc.

17

– Creating step-by-step tutorials for the application domain• Put in extra effort to obtain deeper knowledge about the application domain
and meaningful relationships. This increases the chances to develop usefulsolutions with features that are must-haves, rather than nice-to-haves.
Concrete recommendations are:– Reading textbooks for the domain– Writing a State-of-the-art on the topic– Working within the domain expert’s environment to observe, e.g.,during a sabbatical, a day or week, attending their conferences, etc.
– Having an application domain ‘buddy’, perhaps of the same seniority
to increase availability throughout the project– Hiring people with the right domain background– Organizing semi-structured hands-on workshops– Establish a good working relationship with trust from both sides withyour partners. Follow-up on ideas and give them a win-win situation(contributions in both visualization and domain research)– Develop software that has a higher chance of practical uptake– Developing stable, usable, sustainable software– Considering the best target for the software: integrating code into public projects, developing libraries, standalone or web tools, permissive
licensing– Linking up with existing frameworks that are in use in the domain
and developing a plug-in for these frameworks– For medical visualization, adhering to the ISO standards, ethicalrestrictions, and privacy requirements– Teaming up with a company, for example via an industry-funded
PhD, to develop research results into a productIn addition, we have developed best practices for working with biomedical
data with respect to data privacy and ethical issues:• Follow the GDPR and recommendations from the ethical committees
established at hospitals is crucial.
• Anonymize the data where possible.
anonymization methods available.

There is related work on data

• Use example datasets, if possible, with less ethical/privacy implications:– make your own datasets (scans of authors),– generate or simulate data (accuracy concerns),– explore the use of ‘toy datasets’ for testing, e.g., mouse data.
• Anyone handling the data should sign NDA agreements.

18

Topic 4—Integration & Standardization in BioMedical Visualization
Lars Linsen, David Mayerich, Shuichi Onami, Guido Reina, Baoqing
Wang, Hsiang-Yun Wu
This working group discussed the two issues of (1) standardization of data andmethods and (2) robust methods for integrating multi-faceted data representing
individual samples.
Motivation Modern data in the fields of medicine and life sciences containdata gathered from multiple sources for individual samples. Integrating thisinformation into a joint analysis is required for a comprehensive understanding.
Due to variations in data acquisition, the data from different sources can be ininconsistent formats. Standardization is required to have comparable data.
Comparable data allows for the application of standardized workflows and forcomparative and ensemble/cohort analysis.
Definitions We first provide a set of definitions for terms we have foundcritical to this discussion:• Integration – integrating data from different sources and of different
types.
• Standardization – the process of making something conform to a standard.
• Standard – something used as a measure, norm, or model in comparativeevaluations.
• Multi-faceted data – data sets with samples composed of data frommultiple sources and of multiple types (i.e., patient data composed of MRI,
histology slides, and doctor notes)• Interchange formats – We will have to decide on something even though
nobody will like it. Least a common denominator. We should include anumbrella meta-file describing how separate data are integrated for a single
sample/subject.
Challenges The biomedical community is currently facing a data explosion,
with multi-modal acquisition methods collecting massive amounts of data withlimited tools for analysis. Visualization and analytical tools targeting biomedical
data must therefore overcome barriers imposed by this data explosion. In thissection, we address two tracks for coping with the expanding biomedical data
sets: (1) interchange formats that can cope with the existing and emerging datatypes, and (2) a curated benchmark repository facilitating the standardizationand objective comparisons where possible.

19

Data Types One of the major challenges facing formalization is the broadarray of data types and competing standards. However, we believe that enforcing
an additional standard on an entrenched and constantly evolving biomedical
community is impractical. We therefore advocate for the establishment of a setof interchange formats that maximize data inclusiveness and facilitate conversion
between the existing and future competing standards.
In this section, we will discuss the current variety of basic data types, alongwith the recommended requirements for a formalized interchange format.
• Tabular Data Tabular data refers to data where rows and columns of atable represent the samples (or instances) and attributes (or dimensions),
respectively. The attributes can be of different types, such as numerical(with continuous or discrete range), ordinal, or categorical (or nominal).
They may also contain spatial coordinate values. Tabular data are commonly stored in spreadsheet-like formats. For dynamic data, each time
step is stored in an own spreadsheet with some implicit sorting. There isno common standard on how to do that. A standardization is required.
• Images (multidimensional arrays) One of the most common biomedical data types encountered is image data. Current standards for image
storage, such as bitmaps and lossy-compressed JPEG, are fundamentally
limited to two-dimensional images. This reduces the applicability to a wide
range of common medical images, including MRI, CT, and PET imaging.
Three-dimensional representations, including DICOM and TIFF files, arecurrently limited to three-dimensional data and have limited support for
hyperspectral images, which we have seen a growing use in the form of
FTIR4 and Raman5 methods. One common format available in hyperspectral imaging, primarily used in geospatial imaging, is the ENVI file6 . The
ENVI file format is essentially a RAW memory dump with a corresponding human-readable header file providing details about data format andannotation.
• Text At different steps of acquiring data, there is textual data added,
such as reports in clinical data or annotations. Textual data commonly
consist of an unstructured collection of characters. Structured textual
information (such as markup language-based description) is less common.
Standard formats across different fields of medicine and life sciences are
not established.
• Explicit Geometry Explicit geometry occurs in various fields, e.g., when
extracting the geometry from imaging data or when describing molecular
structures. Example formats include glTF, OBJ, COLLADA, or PLY-files.
• Relationship Data Network is a common model that describes the objects
and their corresponding relationships. Trees, graphs, and matrices are data
structures often used for this purpose. Typical examples include JSON,
xml, property graph, RDF, and others.
4 https://www.sciencedirect.com/topics/earth-and-planetary-sciences/ftir-spectroscopy
5 https://www.sciencedirect.com/topics/engineering/raman-method
6 http://www.harrisgeospatial.com/portals/0/pdfs/envi/Reference Guide.pdf

20• Metadata
Annotations Every interchange format developed should have a corresponding annotation format. This annotation format should provide (1) asemantic segmentation of the source file, (2) an identifier, and (3) links tothe corresponding annotations in other files (other identifier-file pairs).
Provenance Along with the operations that are executed on the data, weneed to store all of them describing the author, date and time, and operation
performed. This provenance information shall allow for reproducible results.
• Multi-Faceted Data Nowadays, all of the facets described above cometogether, e.g., when considering cohorts of people with all kinds of measured
data including imaging data, health record data, and textual descriptions.
This needs to be handled by a combination of multiple standard data
formats. An umbrella data format is required to explain how everythingcomes together.
Benchmarks One major step towards the formalization may be the establishment of a benchmark repository.
Contents of a benchmark repository: This repository should contain awide array of data sets produced by the domain experts and include a range offeatures:• Scalability of extent – Representative data sets should exhibit a range ofsizes, in both extent and resolution. Small data sets are useful for quickly
evaluating the visualization and data mining techniques, while large data
sets are critical for producing scalable algorithms.
• Scalability of dimensions – Data leveraging multiple dimensions, including 3D spatial, hyperspectral, and time-domain images should beavailable. These represent the critical aspects of current and emerging
imaging modalities.
• Scalability of density and complexity – Data sets often contain important data embedded in lower-dimensional subspaces. This is commonly
seen in hyperspectral data, where the spectra are routinely reduced using
dimension reduction (e.g., PCA, ICA). Alternatively, sparse data requires
different representations. A benchmark repository should draw from sparse,
dense, and reducible data.
• Anomalies – Abnormalities, such as outliers, systemic artifacts, and noise,are consistently present in biomedical data. Visualization and analyticalmethods must be robust, and therefore a benchmark repository shouldhouse a realistic range of data exhibiting these anomalies.
Creation of a benchmark repository Formalization requires that benchmarks are well curated and well described. We propose a peer-review process for
hosting within a curated repository. Examples of peer-reviewed repositories areavailable for protocols (Journal of Visualized Experiments) and scientific dataassociated with publications (Nature Scientific Data). The peer review process
should require the submission of the data and associated annotations, along with21

a publication focused on the needs of domain experts. This publication should
describe the methods behind data collection, challenges in data collection (e.g.,
image anomalies), and the desired outcomes for any informatics or visual analysis.
Benchmarks for Calibration One expected barrier in building a benchmark
repository is the wide range of systems involved in data acquisition. We also
recommend establishing a set of normalization standards with the specific goalof mapping data acquired using specific instruments (ex. Nikon, Siemens, etc.)
to a normalized standard. This software should ideally recognize and adjust for
protocol variations, and may integrate standardized phantoms imaged across avariety of systems.
Certification Standardized performance on benchmarks can lead to certification.
To define the certification process we need input from stakeholders to define therequirements.
Standardized workflows for visualization We have no standard systemthat everybody accepts. The main reason for that could be that there are noproper repositories where modules of the system can be added (by people notbeing in-house developers). Having standardized workflows (and maybe evencertificates) would clearly increase the trustworthiness of the visualization tools
and its acceptance by stakeholders.
A standardized workflow could be, on an abstract level, described by:
1. Preprocessing
2. Information extraction
3. Mapping to a visualized extraction
4. Interaction mechanisms
5. Go to 1,2,3, or 4
Figure 6 provides a sketch of this workflow. The implementation of a standardized
workflow for different application scenarios is necessary.

Figure 6: Visualization pipeline.

Outcomes We have discussed the need for data integration as well as standardization of data and methods. We have seen that data is multi-faceted andthat there is a need to incorporate meta-information, such as annotation and22

provenance data. Hence, an umbrella meta-file is necessary for interchange
formats.
Benchmark data sets may be a way towards standardization, but would
require a supervision system to control the collection. Standardized workflows
help to systematize the creation of visualization tools. However, a common
system for joint software development of the community does not exist, although
it would be desirable.

Topic 5—Complexity vs. Knowledge in BioMedical Visualization
Michael Krone, Lars Linsen, David Mayerich, Pere-Pau Vázquez,
Baoqing Wang
Motivation Biomedical data are steadily increasing in complexity, especially
due to the fields of computational biology and bioinformatics. Despite the recent
advances of combined automated and visual data analysis, we (and also domain
experts) still have limited knowledge about the data. For this case, automateddata analyses are not suitable, while semi-automated analysis is still considered
tedious.
Challenges There are many challenges that we need to be addressed in order
to increase the knowledge about data when the datasets become so large. For
the rest of the discussion, we define some terms that are related to this problem:• Heterogeneity: Multi-dimensional data composed of different categories
of information (numerical, categorical, textual, etc.).
• Complexity: Describes how challenging/costly a visualization is to interpret.
• Knowledge: Awareness or familiarity gained by a visualization experience.
• Richness: Amount of information contained or embedded in the data.
With this in mind, we addressed several challenges:• How do we increase knowledge?
• Does the increase in data richness lead us to the increase in knowledge?
Initially, one may think that the increase in the resolution or richness of adataset naturally leads to an increase in knowledge about the data. However, weneed some way to transmit this information to the human. This is challenged by
the fact that the data may be too complex. Therefore, traditional exploratory
visualization techniques may be difficult to apply due to the high number ofparameters implied. Thus, applying such techniques to highly rich data, may
generate a very complex visualization, that may be difficult to understand by
the users. At this point, we may wonder how to measure the complexity of avisualization. And it may also be interesting to be able to set a line between asimple, easy-to-understand visualization versus a too complex one. Unfortunately,
the complexity of a visualization is difficult to measure. Even using the same
23

visualization technique, the result may be easy or difficult to interpret depending
on other factors, such as the data. We discussed on it, and concluded that someof the factors that are highly correlated to the complexity of the visualization
result are the following elements:
Number of independent parameters: The higher the number of independent parameters to represent, the more complex the visualization will be,since we need to devote more visual channels to it, and human perception
is limited.
Dynamic range of each dimension: The number of values a certain parameter can take may further complicate the representation of the variables,
because larger ranges of the perceptual domain will be required.
Number of items: The number of items in each dimension largely varies amongdatasets. For the general, case, we can assume it will be high, and therefore
require a lot of space if individual identification is needed.
Spatial separability of items: Accumulation of items around certain values
may make the visual representations cluttered, while if they can be separated, visualizations may become more understandable.
Sparsity: The distribution of the elements also plays a role, since more space
may be required for the representation.
Hetereogenity: The dimensions of the data may be of different forms (e.g. text,
image...) some of those are easier to represent and more space efficient.
So, for increasing the knowledge, we believe that the improvement of visualization techniques as well as the creation of new automated algorithms that areable to extract information for the data, are required. However, this is not easy,
a number of problems arise: i) The improvement in capture devices as well as
simulation algorithms leads to increase in data sizes, that may generate storage
or data access problems. ii) The increase in data richness also requires higher
computational power to process the data. This is also true for better algorithms.
Transferring new knowledge to algorithms implies higher computational costs.
iii) Finally, the size of screens may be also a limiting factor, not only for desktops,
but even more for portable devices, such as tablets and smartphones.
Outcomes As result of our discussions, we propose some strategies that may
lead to improve our ability to obtain knowledge from large data sources.
An increase in data richness does not (always) need to lead to increased
knowledge. However, this can be tacked by designing new and better algorithms
for data extraction, or new and better visualization techniques. We believe
that, as the knowledge increases, there will be a lower need to develop tools
for exploratory visualization, since the number of confounding variables will
decrease. And, at the same time, the need for tools for confirmatory analysis,
as well as results explanation, will increase. On the other hand, even withnew, more powerful algorithms that work on complex data, may get automatedresults. However, especially in the case of biomedical data, the result can be
unexpected (e.g., a tumor located in a difficult region to treat). Therefore, theneed of exploratory tools will still be required.
24

Topic 6—Reusability, Generalizability, Adoptability in BioMedical Visualization
Marc Baaden, Stefan Bruckner, Issei Fujishiro, Thomas Höllt, Ingrid
Hotz, Shuichi Onami, Guido Reina, Noeska Smit
Motivation The rapid advances in BioMedical visualization have resulted ina large collection of prototypes, visual designs, techniques, and development
kits. These tools are domain- and problem-specific, and cannot become moregeneralized and extensible to other domains. How can we design generalized
applications, which are practical and reusable? How to promote and motivate
sustainability in BioMedical visualization? Can we design a basis for next
generation BioMedical visualization tools? What can we learn from past successes,
where prototypes underwent certification processes and were adopted into clinicalpractice? And, finally, can we determine a way of sharing our designs and results
to the parent field of Visualization? How can we contribute back to the broader
Visualization community?
Challenges One of the main challenges that manifested itself during our
discussions is a perceived lack of incentives for going through the extra effortof creating and maintaining reusable and maintainable visualization software.
Papers are still the main outcomes of many research projects, and with successful
publication there are few benefits for providing access to the research results,
such as the developed software. This is grounded in our community not requiring
researchers to make code available alongside a publication, in contrast to practices
in other communities. Not having a common framework to dock onto also makes
interchange of results, comparison, and cataloguing of the existing solutions
unnecessarily hard. Healthy ecosystems, such as R in the statistics community,
make it possible to require a compatible implementation alongside a publication,
but the fragmentation of our community will prevent such an approach in thenear future. Furthermore, in our discussions we found that many of us believethat there is a certain general unwillingness in the visualization community toadopt frameworks developed by other research groups – as opposed to creating
one’s own solution – leading to the fact that there is a high amount of duplication.
An increased focus on larger collaborative community project would lead to amuch more efficient use of resources.
A related issue is the lack of funding mechanisms for positions dedicatedtowards software engineering. As PhD students, for instance, can only use alimited amount of their time for software development tasks (and may even lack
the required competence), our discussions clearly indicated that stable financing
for such positions is essential. While some countries, such as the UK, have madeadvances in this direction with an institution at the national level [1], in manyothers we have clearly identified limited (often only short-term positions tied to
specific projects) resources dedicated towards such efforts [3, 14, 13].
One consequence of this lack of resources is also that many visualization
software packages suffer from issues related to quality assurance in software
engineering, affecting aspects such as stability, interoperability, and ease of
integration. Efforts need to be made to create solutions that feature clear andwell-defined interfaces, and that can be easily used in a wide variety of different
environments. Most visualization researchers, as well as domain researchers [10],25never receive formal training in software engineering, making it difficult to design
software to be sustainable even mid-term. Best practices regarding permalinks
or DOIs for code repositories are rarely followed, so even if software is madeavailable alongside a publication, it is not consistently referenced and thus hard
to find.
Outcomes We have identified several measures that could help to address theoutlined challenges:• There should be a curated list of visualization software categorized according to application field. This list should be periodically updated to identify”dead” projects, potentially motivating their developers to resurrect them.
Similar repositories are already available in the bioinformatics community,e.g., https://biii.eu/, https://omictools.com, https://bivi.co.
• Visualization software authors should be provided with a set of guidelines on
how to make their software available. These guidelines should address theneeds of other visualization developers, as well as domain users interested
in utilizing the software. We created a draft for these guidelines structured
according to multiple tiers which we include below.
• These guidelines and their different tiers could also be used as a basis forawarding ”reproducibility” and ”adoptability” badges7 , which would be
one way to create additional incentives for authors. These badges could
also be awarded as part of a contest (similar to the IEEE VIS contest, forinstance).
• Overall, the community should strive to award and value contributions insoftware development, by making sure that these efforts are sufficiently
taken into account in, e.g., tenure decisions. In particular, initiatives thatspan multiple institutions should be rewarded accordingly.
• We also identified a lack of standards for different visualization software
frameworks to interoperate, making it difficult to combine or compare theirrespective functionalities. Here, we propose to form a working group thatsuggests simple measures that allow for better communication between
applications (e.g., standardized import/export of camera parameters, option for standardized 3D controls, formalization of selection and orderingwith respect to a data set). The focus of this working group should be
the suggestion of simple and feasible mechanisms for this that can beeasily adopted, as opposed to a complete, but potentially highly complex
standard.
• We could establish a venue for pure application/software output without
the need for a scientific paper. Similar to tools journals in other fields, e.g.,
NAR special issue on web tools8 .
• To increase the impact of VIS tools in the domain, BioMedVis venues
could benefit from being co-located with domain events, such as VCBM
with MICCAI or BioVis with ISCB/ISMB. Senior visualization people
should get more involved with these communities.
7 compare open science badges, https://cos.io/our-services/open-science-badges/
8 https://academic.oup.com/nar/article/46/W1/W1/5047249

26

Appendix: List of tiered requirements for a potential badge system
General prerequisites• Follow FAIR9 principles• Consider best software targets– Implement solutions in established software frameworks– Libraries for easy integration in other tools– Web version (no installation)– Standalone proprietary desktop tools
Requirement tiers for reproducibility (developer target)
Minimum• Provide Readme/Brief description of functionality• Provide exact source code used with paper (e.g., packaged source code,commit id/release when using versioned hosting, etc.)• Provide necessary instruction for building the code on one common operating system• Provide a list of prerequisites (soft- and hard-ware)• Provide citation information• Provide a Point of Contact: e-mail address• Provide licensing information• Provide specification for supported data formats
Better• Use versioned source code; provide repository link, e.g.
– Github– Gitlab• Provide rules for contribution• Provide documentation, e.g., Doxygen• Provide necessary instruction for building the code for multiple platforms• Provide input/output/sample dataset– Can be data from public repositories• Provide a script/GUI/Instructions to reproduce images in paper
Best• Provide advanced User Support, e.g.
9 https://www.go-fair.org/fair-principles/27

– Mailing list
– Issue tracker– Communication platform (Slack)• Provide maximally permissive licensing• Provide DOI, e.g., through zenodo.org• Provide datasets used in the paper• Follow software design principles– Unit tests– Modular libraries– CI/CD• Provide screencast style tutorials
Requirement Tiers for adoptability (end-user target)
Minimum• Provide executable/docker/web application• Provide citation information• Provide user point-of-contact, e.g., e-mail address• Provide specification for supported data formats
Better• Provide a script/GUI/instructions to reproduce images in paper• Provide a basic manual/tutorial on how to use• Provide support for common data formats in the domain
Best• Provide DOI, e.g., through zenodo.org• Provide advanced User Support. e.g.
– Mailing list
– Issue tracker– Communication platform (Slack)• Provide full manual/help system• Provide screencast style tutorials

28Topic 7—Curriculum: How can we structure a BioMedical
Visualization course?
Johanna Beyer, Daniel Haehn, Thomas Höllt, Ingrid Hotz, Timo
Ropinksi, Thomas Schultz, Pere-Pau Vázquez
Motivation A possible approach to creating a formalization of biomedical
visualization is to use teaching as a driver for this formalization. Also, we
observe that the challenges while creating a curriculum are similar to the generaldifficulties of formalization of our field. We investigate how we can structure abiomedical visualization course that covers the diversity of students, content,
and research interests. Our goal is to teach the full spectrum of students fromcomputer science, technical biomedical fields, as well as the life sciences.
Challenges Coming up with a curriculum requires taking into account theboundary conditions of the course to find the right content for the right audience.
We identify the following challenges for teaching biomedical visualization:
1. Our students come from diverse fields with different backgrounds anddifferent goals. A curriculum for computer science students should focus
on technical visualization methods and programming. In contrast, thecurriculum for life science students should focus on the application ofvisualization in the domain.
2. A BioMedVis course must include the basics of the area the students
are least comfortable. This can, for example, be the basis of hypothesis
formation for CS students or basic scripting for the life science students.
3. It is necessary to find the right level between teaching basic knowledge invisualization and the specific demands of the applications. There are only
a few application-specific techniques and methods that are only applicable
in BioMedVis and not in a general VIS context.
4. Different universities have different courses that can be used as prerequisites.
We need to adjust a curriculum to other courses available in the environment
and the level of the course.
Parameters
Boundary Conditions
Audience / Skill-Level
Other Courses / Prerequisites at the University
Time
Toggles
Personal Interest of the Instructor
Theory vs. Applied

Figure 7: A formalized curriculum has to respect the boundary conditions of acourse and take into account toggles based on the instructor’s preferences.

29

Existing Materials We found the following interesting links to the existingmaterials. These links are a good starting point and a valuable source for building
a curriculum.
• Bernhard Preim’s Lecture https://medvisbook.com/courses-and-tutorials/
prof-preims-international-medvis-lectures/• Bernhard Preim’s Book https://medvisbook.com/about/• Course at Delft https://www.tudelft.nl/en/health/education/medicalvisualization/• Visualization in Medicine Book https://www.sciencedirect.com/book/9780123705969/visualization-in-medicine• UChicago MS in Biomedical Visualization https://catalog.webhost.
uic.edu/gcat/cat1012archive/AHBVIS.shtml• Richard Robb’s Book https://www.amazon.com/Biomedical-ImagingVisualization-Analysis-Richard/dp/0471283533• Omic’s course https://nbisweden.github.io/workshop_omics _integration/
Outcomes The generation of a curriculum is not a rigid process. To copewith the challenges identified above, we propose to build up the curriculumfrom basic building blocks. To create a learning path, a teacher selects theappropriate blocks for a specific course. This involves defining a custom learning
path throughout a complex space of building blocks, compared in Figure 8.
We have identified three main topics that make up the building blocks: basic
techniques, domain-specific techniques, and use cases. The use cases represent anessential part of teaching the theory with ties to complex domain applications.
These building blocks should include basic and advanced concepts that we
can also tune according to the boundary conditions and toggles. An Overview
also helps at the beginning of the course to keep students motivated and hungry
for knowledge.
Example Curriculum: We defined the following building blocks to cover many
aspects of biomedical visualizations.
• Overview/Motivation/Intro to the field– Tasks– Diagnosis– Surgery/Intervention– Clinical Research– Training / Education• Basic Blocks– Data Types∗ Data Structure∗ Interpolation / Data Structure30

Overview/Motivation

Basic Blocks
Data Types
Image Processing
Visualization and Visual Mapping
Perception
Design Methodology
Interaction

Domain-Speciﬁc Content
Image Visualization
Molecular Visualization
Omics Visualization
Aggregated / Combinations / More Advanced

Use Cases
Diagnosis
Surgery / Intervention
Clinical Research
Evaluation

Figure 8: We distinguish between different types of building blocks. The
Overview/Motivation allows increasing the students’ interest in biomedical
visualization at the start of the course. Basic blocks can then adjust different
audiences to similar starting levels. Domain-specific content includes the mainareas of biomedical visualization. Finally, use-cases tie in the theory with domain
applications.
∗ File Formats∗ Basic Data Acquisition– Image Processing∗ Alignment/Registration∗ Segmentation∗ Filtering– Visualization and Visual Mapping∗ Direct Volume Rendering∗ Isosurface∗ Flow Vis· Streamlines· Particles∗ Info Vis∗ Dimensionality Reduction– Perception– Design Methodology∗ Evaluation– Interaction∗ Advanced Queries∗ Brushing/Linking

31∗ Pan/Zoom∗ Filter• Tasks / Drivers / Use-Cases– Diagnosis– Surgery / Intervention– Clinical Research– Evaluation• Image Visualization– Structural∗ 2D/3D/4D∗ Projection Methods· MPR/CPR· Virtual Endoscopy∗ Diffusion Imaging· Tractography/Glyphs– Functional∗ fMRI∗ Bloodflow∗ Nuclear Imaging• Molecular Visualization– Rendering Representations∗ Ball and Stick∗ Surfaces∗ Meso Scale– Molecular Surfaces– Abstraction– Tunnels– Dynamics• Omics Visualization– Sequence Alignment– Sequence Search• Aggregated / Combinations / More Advanced– Multi-Modal Visualization– Ensemble Visualization– Comparative Visualization– Quantitative Visualization– Illustrative Visualization
32

Retrospective The field of biomedical visualization is very complex. It isseemingly impossible to define the ultimate curriculum. Therefore, we propose
a repository of building blocks that can be combined based on the specificboundary conditions and toggles of an individual course.

Topic 8—Dissemination: Driver of Formalization in BioMedical Visualization
Daniel Haehn, Barbora Kozlı́ková, Martin Krzywinski,
Johannes Sorger, Hsiang-Yun Wu
Motivation Dissemination is a central part of academia, transferring knowledge to peers, students, and the public. Without dissemination, research iscreated in a bubble and innovation is stiffled. To consistently create effective
dissemination, the researcher needs a way to formulate their message. Providing
form to the message implicitly drives a formalization of the underlying workthrough common vocabulary, metaphors, and conventions. Dissemination canthus be seen as a driver of formalization.
During this meeting, we discovered that the community reflected the assumption that the output of research feeds into the process of dissemination and theprocess of formalization (9a). While this is undoubtedly true–the creation of
new abstractions and schemas for advancing thinking and analysis does arise outof research–the dissemination process has an equally powerful (or even greater)
influence on the creation of formalisms.
Challenges Because it is extraordinarily difficult to meaningfully create adeliverable that generally improves on the process of how research impacts
formalism, we aimed to instead provoke discussion by proposing a light-weightformalization for dissemination.
Motivated by the initial question of how we can improve science dissemination,
we aimed to establish the forms which dissemination can take, which finally led
us to create a formalization of dissemination itself.
The driver of our discussions were questions that address general aspects ofthe communication process (1) the presenter, (2) the audience, (3) the purposeof dissemination (intended outcome), (4) the medium (e.g., video, audio) andplatform (e.g., social media, meeting), and finally (5) the overall packaging,which includes aspects such as tone, style, length, lifespan, level of engagement,interactivity, and accessibility.
We consider that at this level of abstraction it is not necessary contextualize
the scheme to BioMedVis because this is something that is encapsulated in the
payload of the message, which is always up to the researcher to determine.
Outcomes
Schema Our formalism resulted in a dissemination framework. The framework
can be split into seven aspects of dissemination, each describing an essential partof dissemination (9), e.g., who is the sender, who is the receiver of the message,
through which medium is the message conveyed? Each aspect is described bya list of representatives (e.g., ”To”: the public, peers, students, etc). We see

33the list of aspects as exhaustive, while the list of representatives can be further
extended.
Special consideration need to be given to ”the message”, and ”the package”.
The message is the domain-specific payload of the dissemination. Itself it canbe broken down into sections (e.g., traditionally: introduction, aims, methods,
results, conclusion) but this is up to the researcher to arrange and should bedone so to match the profile of the dissemination. The package describes theway in which the message is delivered, independent of the medium. The package
contains several sub-aspects, such as the style and tone of a message, the length,
the engagement factor (immersive/interactive), etc.
The initial intent of this framework was to make the ”design space” ofdissemination explicit. However, the entire framework can also be seen as achecklist, clarifying (to the sender of a message), who they want to reach and how
they want to reach them – thus acting as a guideline for science dissemination.
Making the extent of dissemination explicit, opens several options for applying
this framework.
A single dissemination profile (e.g., for a tutorial video) would be specified
by items in the checklist. A group of multiple profiles would be considered a”campaign”, which is a collection of disseminations over various time and various
media and platforms. For example, one set of disseminations might aim to
inspire while another clarify.
Online platform We propose a web-based platform that can act as a checklist
builder and repository of effective examples, tips and caveats for specific combinations of settings (e.g., public inspiration video of less than 2 minutes in length)(10a). This functionality would allow researchers to plug into communication
strategies and gain awareness of innovative options for sharing their knowledge
and research.
Extensibility This framework would classify existing disseminations, assigning an explicit dissemination profile to them. The profile space itself could beformalized more rigorously into structured metadata (similar to exif data in images and subsections such as IPTC) that would be attached to each dissemination(and drive semantic searching).
As a domain of study becomes more mature, our dissemination checklist
would be expanded to include a list of all (or merely those that are relevant)formalizations in the field. The researcher would use this list to identify thosethat are adhered to in the dissemination and, equally importantly, those that
could be adhered to but are not (thereby embed in the checklist a challenge of
how best to adhere to formalisms). This would help creating communicationthat is consistent and emphasize the value of closing the loop from dissemination
to formalization.

34

Formalization

Research

Dissemination

Figure 9: Relationship between formalization, research and dissemination. Formalization is influenced by both research and dissemination and we argue thatthe latter has a stronger effect.
MessageFrom

MediumTo

Outcome

Package

MessageFrom

MediumTo

Outcome

Package

M1

Domain expert

Audio

PublicInspiration

Style/Tone (Positive/Negative)

M1

Domain expert

Audio

PublicInspiration

Style/Tone (Positive/Negative)

M2

Researcher

Talk

Domain expert

Clarification

Time (30s, 2min, 5min, longer)

M2

Researcher

Talk

Domain expert

Clarification

Time (30s, 2min, 5min, longer)

M3

Students

Video

Peers

Grow community

Engagement (R, AR, VR)

M3

Students

Video

Peers

Grow community

Engagement (R, AR, VR)...

Service provider

StaticCustomer

Education

One-shot (Yes/No)...

Service provider

StaticCustomer

Education

One-shot (Yes/No)

Organization

Mixed

Unknown

Money

Interactive (Yes/No)

Organization

Mixed

Unknown

Money

Interactive (Yes/No)

Edutainment

Accessibility (legibility, contrast,
color-blind, !C.S.)

Unknown

Edutainment

Accessibility (legibility, contrast,
color-blind, !C.S.)

Unknown

Edutainment

Edutainment

Awareness

Awareness

Part of record

Part of record

Feedback

Feedback

Provocation

Provocation

(a)

(b)

Figure 10: Collected factors for the campaign builder, including (a) a discussed
checklist, and (b) an example.

35

Conclusions
During this 4-day seminar, we brought together 25 visualization experts fromdiverse institutions, backgrounds and expertise to discuss, identify, formalize, anddocument the specifics of our field. The main aim of the seminar was to formalize
the current status of the field of BioMedical Visualization and to determine
future challenges. A free and open discussion between all participants has beensought—opening novel, interesting paths for the future and contributing to new,creative collaborations. This has been a great opportunity to cover a range of
relevant and contemporary topics, and as a systematic effort towards establishing
better fundaments for the field and towards determining novel future challenges.
Eight main topics have been discussed in the form of working groups and were
presented in this report. These are the following:
1. Differences between BioMedical Visualization and the parent field of Visualization.
2. Where formalization is needed for BioMedical Visualization?
3. Best Practices in BioMedical Visualization
4. Integration & Standardization in BioMedical Visualization
5. Complexity vs. Knowledge in BioMedical Visualization
6. Reusability, Generalizability, Adoptability in BioMedical Visualization
7. Curriculum: How can we structure a BioMedical Visualization course?
8. Dissemination: Driver of Formalization in BioMedical Visualization

36

List of Participants• Marc Baaden, CNRS Paris,France• Johanna Beyer, Harvard University, USA• Stefan Bruckner, University of Bergen, Norway• Issei Fujishiro, Keio University, Japan• Daniel Haehn, University of Massachusetts Boston, USA• Helwig Hauser, University of Bergen, Norway• Thomas Höllt, Leiden University Medical Center / TU Delft, Netherlands• Ingrid Hotz, Linköping University, Sweden• Tobias Isenberg, INRIA, France• Barbora Kozlikova, Masaryk University, Czech Republic• Michael Krone, University of Tübingen, Germany• Martin Krzywinski, Canada’s Michael Smith Genome Sciences Center at
BC Cancer, Vancouver, Canada• Lars Linsen, University of Münster, Germany• David Mayerich, University of Houston, USA• Shuichi Onami, RIKEN Center for Biosystems Dynamics Research, Japan• Renata Raidou, TU Wien, Austria• Guido Reina, University of Stuttgart, Germany• Timo Ropinski, Ulm University, Germany• Thomas Schultz, University of Bonn, Germany• Noeska Smit, University of Bergen, Norway• Johannes Sorger, Complexity Science Hub Vienna, Austria• Pere-Pau Vázquez, Universitat Politecnica de Catalunya, Spain• Baoqing Wang, Keio University, Japan• Hsiang-Yun Wu, TU Wien, Austria

37

Figure 11: The participants of NII Shonan Meeting No.167 on “Formalizing
Biological and Medical Visualization”.

38

Figure 12: The poster of NII Shonan Meeting No.167 on “Formalizing
Biological and Medical Visualization”.

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