Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Volume 1 Issue 6, June 2019

Learning an atlas for the brain

Deep learning methods provide a powerful tool for the processing of biological and medical images. In this month’s issue, a deep neural network is used by Iqbal et al. for robust registration of brain images across different stages of brain development, and by Shan et al. to accurately reconstruct medical computerized tomography scans performed under low radiation doses. This issue also features an interview with Effy Vayena, who discusses the UK National Health Service’s recent code of conduct for using such AI-based systems in healthcare.

See Iqbal et al., Shan et al. and Q&A with Vayena

Image: Asim Iqbal and Romesa Khan, University of Zurich and ETH Zurich. Cover design: Karen Moore.


Top of page ⤴


Top of page ⤴

Comment & Opinion

  • The European Commission’s report ‘Ethics guidelines for trustworthy AI’ provides a clear benchmark to evaluate the responsible development of AI systems, and facilitates international support for AI solutions that are good for humanity and the environment, says Luciano Floridi.

    • Luciano Floridi
  • To develop scientific methods for evaluation in robotics, the field requires a more stringent definition of the subject of study, says Signe Redfield, focusing on capabilities instead of physical systems.

    • Signe Redfield
  • Effy Vayena runs a lab at ETH Zürich that studies ethics, legal and social implications of precision medicine and digital health. We asked her views on the code of conduct for using artificial intelligence (AI) systems in healthcare, recently published by the UK’s National Health Service (NHS).

    • Liesbeth Venema
Top of page ⤴

Books & Arts

Top of page ⤴


  • Reducing the radiation dose for medical CT scans can provide a less invasive imaging method, but requires a method for reconstructing an image up to the image quality from a full-dose scan. In this article, Wang and colleagues show that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

    • Hongming Shan
    • Atul Padole
    • Ge Wang
  • High-throughput brain image registration methods that are independent of any pre-processing steps, and are robust under mild image transformations, could accelerate the study of region-specific changes in brain development. A deep learning-based method is therefore developed for automated registration through segmenting brain regions of interest with minimal human supervision.

    • Asim Iqbal
    • Romesa Khan
    • Theofanis Karayannis
Top of page ⤴

Challenge Accepted

  • Rebuilding particle trajectories from high-energy proton collisions is an essential step in processing the petabytes of data generated by the Large Hadron Collider at CERN. In search of an order of magnitude speed-up, physicists reached out to the computer science community.

    • David Rousseau
    Challenge Accepted
Top of page ⤴

Amendments & Corrections

Top of page ⤴


Quick links