| EPSRC Reference: |
EP/N023668/1 |
| Title: |
QuantifyTBI: A Machine Learning Approach to Automatic Segmentation and Quantification of Lesions in Traumatic Brain Injury Imaging |
| Principal Investigator: |
Glocker, Dr B |
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| Department: |
Computing |
| Organisation: |
Imperial College London |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 June 2016 |
Ends: |
31 May 2017 |
Value (£): |
97,534
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| EPSRC Research Topic Classifications: |
| Med.Instrument.Device& Equip. |
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Summary on Grant Application Form |
Traumatic brain injury (TBI) has been characterised as the most complex disease in the most complex organ of our body. TBI is defined as a pathological change in brain function caused by strong external force, commonly induced by falls, assaults, car traffic accidents, sport injuries, or the blast of an explosion in military combat. TBI has been estimated to affect over 6.8 million people per year worldwide and is the leading cause of disability and death of young adults in developed countries. The high number of incidences puts a major socio-economical burden on public health. A recent estimate of the total costs of TBI in Europe, excluding non-hospitalised patients, produces a figure of 33 billion Euros. The biggest cost, of course, is paid by the millions of patients and their families, who live for years with long-term consequences of TBI. Medical imaging combined with advanced computational methods have the potential to improve TBI care by supporting the critical tasks of early diagnosis, prognosis, and treatment. Imaging has been established as the primary tool for visual, non-invasive assessment of TBI both in critical care, and short- and long-term follow-up. However, the current use of imaging for TBI assessment is limited to manual, qualitative and often subjective inspection of the images. This motivates the main objective of this project which is the development of software tools that enable the automatic extraction of clinically useful information to improve care for patients with TBI.
The project QuantifyTBI explores computational methods, in particular machine learning approaches, to analyse and quantify brain scans of patients with TBI. Specific algorithms and software tools are developed that allow doctors to more objectively and accurately assess the severity of head injuries and monitor the progression during treatment. The main focus is to develop software that allows to automatically derive quantitative measures about TBI lesions from the patient's brain scans. Such measures include the number of lesions, the type of lesions, their size, the location, and the ratio of affected brain tissue. An accurate and comprehensive image-based quantification is essential for developing personalised treatment strategies, supporting diagnosis and monitoring disease progression. It also helps to better understand TBI from a clinical perspective, and will eventually lead to better treatment and improved outcome of TBI patients.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.imperial.ac.uk |