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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.
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.
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.
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.