Machine intelligent state-of-the-art medical image analysis
MIRA Next Generation Machine Intelligence for Medical Image Representation and Analysis Project Objectives
Medical imaging plays an important role in modern medicine, helping doctors to diagnose diseases and monitor their development, yet detecting subtle signs of early disease in medical scans can be challenging even for expert radiologists. Researchers in the MIRA project are using machine intelligence to develop algorithms that will enhance the clinical workflow, as Dr Ben Glocker explains. The volume of medical images produced in hospitals has increased significantly over recent years, yet the number of staff trained to analyse and interpret these images has not kept pace. With healthcare organisations looking to both diagnose patients more rapidly and reduce costs, technology has an important role to play in medical image analysis, a topic at the heart of the MIRA project. “We are essentially trying to extract and quantify information in medical images, that can then be used to inform clinical decisions,” explains Dr Ben Glocker, the project’s Principal Investigator. The aim here is to develop algorithms to analyse medical scans in a more objective way, essentially turning images into clinically useful information. “A lot of our work is about predictive modelling, so basically building models that can make predictions for new data by learning from previous examples,” continues Dr Glocker. “We’re also looking at diagnosis; for instance, can we classify which tumours are aggressive and which are benign?”
The ability to discover groups of subjects and identify characteristics that may lead to similar health issues could allow medical staff to probe deeper into the major factors behind population diseases. A key challenge here is developing algorithms that can meaningfully combine imaging and non-imaging information. “There’s a lot of interest on our side in how we can combine these different data sets,” says Dr Glocker. Another major priority is ensuring that machine learning methods are sufficiently robust to deal with data from different sources. “It may be that the type of data changes. Maybe the data was acquired in a different way, with a different scanner,” explains Dr Glocker. “If you have an algorithm that can detect certain types of brain lesions, will it still work when you deploy it in a new setting with a new scanner and new algorithm? Or maybe a different patient population?”
Trustworthy models
Machine learning This work is based on using the power of machine learning. Dr Glocker and his colleagues in the project, including research assistants, PhD students and post-docs, are making extensive use of publicly available data, such as 30,000 Computed Tomography (CT) images, which have been annotated by clinical experts. “We use that as training data for our algorithms, which then go through all these examples and try to identify patterns of disease. So what can we look for in a scan that distinguishes a lesion from the normal anatomical appearance?” he outlines. It can be difficult to detect lesions in 3D CT images, as they are often very small, so the project’s work could have a significant impact. “We aim to build systems that can automatically find very subtle signs of disease at very high levels of accuracy across the whole body,” says Dr Glocker. “We try to help doctors by automatically highlighting suspicious regions, where it might be worth taking a closer look.” A further dimension of the project’s work centres around the use of unsupervised
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A machine learning algorithm is able to recognise individual organs in a whole-body MRI scan providing a detailed patient-specific anatomical map.
representation learning, where the training data has not been labelled. This approach can be used to discover whether there are specific groups within a wider population that share similar characteristics, which Dr Glocker says may be important in understanding the causes of certain diseases. “What factors are linked to certain developments?” he asks. By examining data from the UK Biobank Imaging Study, researchers in the project hope to help create a fuller picture. “This is an interesting dataset. There’s comprehensive imaging data gathered from random members of the population, including brain, abdominal and cardiac imaging,” outlines Dr Glocker. “Those people also report about their lifestyle, health and demographics. They are asked to answer questionnaires, for example whether they smoke, do sports or if they work nightshifts and their education level.”
This is central to building trustworthy and robust computational models. The technology must be able to still produce reliable results when circumstances change, for example if a hospital acquires a new scanner, a topic that Dr Glocker and his team are addressing. “We can take a prediction, and we can see if that prediction itself makes sense, such that it would allow us to explain the data for which we have the true answer,” he outlines. Researchers in the project have developed a concept called reverse classification accuracy, which acts as an effective quality control mechanism. “We essentially go back to our training data and ask; ‘if we take predictions from new data sets, and we look back at our training data, how well do these new predictions explain our training examples?’” explains Dr Glocker. “If we can explain the training data effectively, then the hypothesis is that the predictions are of good quality.” A reliable set of computational tools could be invaluable in the clinic, giving medical staff deeper insights into the nature of an individual case and its likely progression. Within the project, collaborations have been
EU Research
Project MIRA is aiming to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. An overarching objective is to harvest information from population data to construct the most advanced statistical models of anatomy. This will provide insights into complex diseases, and enables a novel approach to abnormality detection that aims to automatically find subtle signs of pathology in medical scans.
Project Funding
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 757173, project MIRA, ERC-2017-STG). Left: An algorithm trained to automatically segment brain lesions matching the performance of a human expert. Right: Another algorithm has learned to spot subtle signs of disease and flags up suspicious regions to the physician.
established with radiology departments, with researchers aiming to develop technologies that will support the clinical workflow. “Clinicians would look at a CT scan, and we would try to enhance that by flagging up things that we find suspicious, where they might want to take a second look,” outlines Dr Glocker. These technologies can be used in different ways, sometimes playing a complementary role to human staff, while in other circumstances they might replace them entirely. “A machine could run in the
this data. There’s a lot of potential to use new technology in healthcare, and not just in adding technology to existing workflows, but also new types of medical diagnostics based on imaging” he stresses. At this stage, Dr Glocker is focused more on helping improve diagnosis however. “There is known to be a high risk of human error with certain pathologies, where we know that radiologists might miss certain types of lesions,” he says. “If we can improve on that, then that will be an important step forward.”
A lot of our work is about predictive modelling, so basically building models that can make predictions for new data by learning from previous examples. We’re also looking at diagnosis, for instance can we classify which tumours are aggressive and which are benign? background and provide a safety net, making sure that the human expert has not missed anything. If they both agree then we can have a higher degree of confidence that the decision is correct,” says Dr Glocker. The role of technology in healthcare is evolving, as new methods and techniques are developed which promise to enhance the clinical workflow and improve efficiency. While technology is not always the answer to every problem in healthcare, Dr Glocker believes it does have an important role to play in medical image analysis. “The number of images that we get from each patient and the resolution are increasing, and you do need technology to get the most out of
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Contact Details
Project Coordinator, Dr. Ben Glocker Reader in Machine Learning for Imaging Imperial College London, Department of Computing 180 Queen’s Gate, London SW7 2AZ, United Kingdom T: +44 20 7594 8334 E: b.glocker@imperial.ac.uk W: http://project-mira.eu W: www.doc.ic.ac.uk/~bglocker TW: @GlockerBen Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas, Ben Glocker / Domain Generalization via ModelAgnostic Learning of Semantic Features / Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019 Martin Zlocha, Qi Dou, Ben Glocker/ Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels/ International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
Dr Ben Glocker
Dr Ben Glocker is Reader (eq. Associate Professor) in Machine Learning for Imaging coleading the Biomedical Image Analysis Group at the Department of Computing, Imperial College London. He holds a PhD from TU Munich and was a post-doc at Microsoft and a Research Fellow at the University of Cambridge. His research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving imagebased detection and diagnosis of disease.
Machine learning powered image analysis enables accurate quantification of disease as here shown for a patient suffering from a traumatic brain injury providing important information for clinical decision making.
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