2. Panel Discussion Part I Methodology

3. Ideas from adult MR brain segmentation are used in neonatal MR brain segmentation. However, additional challenges in neonatal brain segmentation require more advanced methods. Could these methods be used again in adult MR brain segmentation to improve performance and increase robustness? Adult MR Brain Segmentation Neonatal MR Brain Segmentation

4. Is an all-in-one approach for MR brain tissue segmentation in data from neonates to adults (and everything in between) desirable? Neonates Adults MRI at different time points pediatricsadolescents

5. Method development in medical image analysis often depends on the available data. Are we limited by tuning algorithms on small datasets? Do we focus enough on handling datasets with pathologies (which are of more clinical interest)? Method Development Evaluation

6. Could we develop more advanced and more robust algorithms if a wide variety of data would be available? MR Acquisition Age Pathology MR Acquisition

7. Could we develop more advanced and more robust algorithms if a wide variety of data would be available? MR Acquisition Age Pathology MR Acquisition Do we need to fill this space before we can succeed?

8. Adult MR brain segmentation is one of the oldest medical image analysis problems. However, there is still no method that is used in clinical practice? Would more extensive evaluation help here? What is the role of researchers? What about the role of industry? Decisions in the setting of clinical neurology mostly rely on qualitative (ie, visual) interpretation of structural brain images. On visual assessment, for example, global brain atrophy is recognized by an increase of cerebrospinal fluid (CSF) spaces and shrinkage of parenchymal structures. Giorgio, A., De Stefano, N. (2013), “Clinical Use of Brain Volumetry”, JMRI, 37(1), 1–14. Are automated segmentation tools really needed?

9. Do we need to join forces? Combine expertise? Combine methods? Combine results of different methods? Share data?

10. Panel Discussion Part II Data Sharing

11. Reasons for Sharing Data Economic Cost of repeated data acquisition without new insights May lead to community efforts for development and validation of image processing tools Avoid replication of existing data and tools  Maximize investment from funders

12. Reasons for Sharing Data Engagement with patients and general public Volunteers and patients give their consent for contributing to new knowledge and discovery Maximize participation of patients and volunteers while keeping their right to opt out

13. Reasons for Sharing Data Scientific Prevents selective and biased data analysis and reporting Possibility to replicate results, to identifiy sub- optimal or even wrong solutions

14. Reasons for Sharing Data Credits Sharing data can be rewarding for funding and publishing.

15. Advantages Journal paper about the data (credits, citations, clear description) Peer reviewed … Sharing Data via dedicated data journal (e.g. Nature Scientific Data) Disadvantages No direct comparison of methods Risk that only special datasets will be accepted …

16. Sharing Data Is it our responsibility as medical image analysis researchers? Is it important for us to share data? Or rather to share evaluation data (e.g. manual segmentations)? We actually do it with challenges… Could we take this one step further?

17. Advantages Direct comparison of methods Opportunity to write a journal paper … Sharing Data: Challenges Disadvantages Organizing a challenge is time consuming After a challenge is set up, you cannot add data anymore …

18. How to Share Data? Do we need standardization? Do we need quality control? – Ratio cost / added value – Automatic vs. Human Quality Control – Crowdsourcing: Let people rate the data? Infrastructure Issues – Data sharing repositories: Big Data Centers vs Distributed Storage vs Peer to Peer storage (bioTorrent)? – Code sharing: Is it different from data sharing?

19. How to Share Data? There are no technological barriers for data sharing! But sharing code is more difficult (and perhaps less desirable/acceptable)?

20. How to Share Data? How do we reward data sharing? Should we pay for access to data, especially if data has been annotated?

21. How to Share Data? How can funding agencies/institutions help to promote data sharing? Should funders decide which direction our field develops? Or should we as a technical community decide what we need?

22. Closing Remarks