Infrastructure for Algorithm Comparisons, Benchmarks, and Challenges in Medical Imaging

AuthorAuthors: Jayashree Kalpathy-Cramer and Karl Helmer

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Figure 5. Portal for Kaggle, a leading website for challenges for data scientists

Topcoder

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is a similar popular website for software developers, graphic designers and data scientists. In this case, participants typically share their code or designs. They use the Appirio proprietary crowdsourcing development platform, built on Amazon Web Services, Cloud Foundry, Heroku, HTML5, Ruby and Java. A recent computational biology challenge run on Topcoder demonstrated that this crowdsourcing approach produced algorithmic solutions that greatly outperform commonly used algorithms such as BLAST for sequence annotation {Lakhani, 2013 #3789}. This competition was run with a $6000 prize and drew 733 participants (17% of whom submitted code) and the prize-winning algorithms were made available with an open source license.

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Figure 7. Example Challenge hosted in Synapse

COMIC framework

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is an open-source platform that facilitates the creation of challenges and has been used to host a number of medical imaging challenges. The Consortium for Open Medical Image Computing (COMIC) platform, built using Python/Django was created and is maintained by a consortium of five European medical image analysis groups including Radboud University, Erasmus, and UCL. They also offer a hosted site, with the hardware located at Fraunhofer MEVIS in Bremen, Germany. The current framework allows participants to create a website, add pages including wikis, create participant registrations, methods for organizers to upload data and participants to download data (for instance through Dropbox). However, the platform including ways to visualize medical data and results is still under development as are options to share algorithms and perform challenges in the cloud.

The main steps to create a new challenge

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are:

• Create a project

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• Add pages

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• Making uploaded files available for download
• Allowing others to register for your project

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• Allowing others to register for your project

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• Make your Make your project appear in the projects overview

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• Allow file uploads
• Including content from files on a page
• Allow others to edit the project
• Changing colors and other styling
• Project data folder

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• Including content from files on a page

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• Allow others to edit the project

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• Changing colors and other styling

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• Project data folder

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• Page permissions

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  Page permissions

However, at this time, there is limited support for automatic evaluation of submitted results, results presentation, native support for medical images although many of these features are planned.

The HubZero

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is an open source platform developed for scientific collaboration. It has been used heavily in a number of communities including nanoscience, earthquake engineering, molecular diagnostics and others. A version focused on cancer informatics can is hosted at nciphub.org. nciphub shares a lot of features with the Synapse platform. It allows user management and role-based access. Users can create groups that share common interest and collaborate within these groups. Files can be shared within projects. Other features include wiki, calendars, creating and sharing resources such as presentations, multimedia and even tools. Most common tools found on the various hubs are those based on simulations. Although nciphub has limited native support of medical imaging, libraries to handle medical images can be configured to work in the hub. Members of the Quantitative Imaging Network (QIN) are exploring the use of nciphub for challenges, especially for the communication and data sharing. for challenges, especially for the communication and data sharing.

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CodaLab

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CodaLab is an open-source project that originated at Microsoft Research that was expressly created for hosting challenges and supporting reproducible research. The OuterCurve Foundation currently maintains it. Challenge organizers can easily set up challenges by creating a competition bundle that consists of data as well as evaluate tools. As part of the configuration files, the number of phases and duration (e.g. training, leaderboard, test) can be set up by the organizer. The evaluation program can be written in any language. Participants can upload results and get immediate feedback. The currently available version of CodaLab comes with scoring algorithms for image segmentation evaluation Organizers can extend the presentation of results to allow drilling down into the results with tables and charts. CodaLab currently uses the Azure platform although, in theory, it should be possible to deploy on other servers without a great deal of effort. CodaLab is also developing support for worksheets. These are resources to support reproducible research and for collaboration. Using these, researchers have compared a number of open source NLP tools on different public datasets. As this technology continues to be developed, researchers will be able to quickly compare the performance of different algorithms on a range of datasets in the "cloud" by leveraging Azure technology.

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The MIDAS platform has been used to host a couple of imaging challenges. A special

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module is available to host challenges. The developers of the platform also made available the COVALIC evaluation tool for segmentation challenges with the following metrics: Average distance of boundary surfaces, 95th percentile Hausdorff distance of boundary surfaces, Dice overlap, Cohen's kappa, Sensitivity, Specificity, Positive Predictive Value.

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The web portal is the single point of entry for the participants. Historically, this would have information about the challenge, potentially host the data and provide a submission site for the user to upload results. The challenge organizer could also provide the results of the challenge at this page. Many challenges have wikis and announcement pages as well as forums. A good example of active discussion forums can be found at the Kaggle

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. Most systems have backend systems (typically a relational database) for managing data and users. These allow registered used to access perhaps the training data and ground truth, the test data but not the ground truth.

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