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Key advances in the earlier diagnosis of cancer, leading to better treatment and higher survival rates, have resulted from the commercialisation of unique imaging software that exploits research from the Department of Engineering Science. The software products that came from this research, Volpara™, XD and XRT are now used at major cancer centres worldwide (with approximately 1100 software installations), aiding treatment of tens of thousands of patients every year. Between 2009 and July 2013, Volpara™ scanned over 1.2 million mammograms, enabling the early detection of around 1800 cancers. The products' success has catalysed significant improvements in cancer care, and generated an estimated £9M in sales over the past two years for the spinout companies established to develop them (Matakina, based in New Zealand, and Mirada Medical, based in the UK).
UCL's research has led to changes in patient care for men with prostate cancer, through the implementation of less invasive, image-directed treatment and diagnostic strategies, and clinical trials that use these techniques. The use of medical image registration software to deliver high- intensity ultrasound therapy in a targeted manner has been shown to change the treatment plan in half of the patients participating in a clinical study. New biopsy criteria are now used routinely to classify patient risk at University College Hospital, where, since 2009, clinicians have determined the treatment options for more than 741 prostate cancer patients. The scheme has been adopted, by 15 other hospitals in the UK and internationally, where it has become the recommended standard of care, and has been used to treat more than 1,200 patients.
A biomarker is a measurement or physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions, or survives. Biomarkers can be used to assess changes induced by a therapy or intervention on a clinically meaningful endpoint.
New quantitative image analysis techniques developed at Imperial College have enabled the computation of imaging biomarkers that are now widely used in clinical trials as well as for healthcare diagnostics. This case study illustrates the resulting key impacts including:
The key impact of this project, in the form of `proof of concept', has been by influencing the practice of medical professionals (haematologists) at the Transfusion Medicine & Immunohematology section (in the hospital wing) of the Christian Medical College (CMC) Vellore (India). This has been achieved by developing and implementing system software for segmenting (and watermarking) of the nuclei of the White Blood Cells (WBCs) of peripheral blood smear images to overcome the challenge of identifying various pathological conditions. Segmentation of medical images is a highly challenging process, especially when dealing with blood smear images, which are known to have a very complex cell structure. The project has led to a significant improvement in the work process of haematologists at CMC's hospital wing where the output of this research (software system pilot) is being used. This has had an impact on the way smear slides are digitised, archived, and includes the segmentation, analysis, and watermarking of medical images at CMC. Christian Medical College (CMC) and Hospital at Vellore is an educational and pioneering research institute and a tertiary care hospital (which is the CMC's hospital wing), located at Tamil Nadu in Southern India.
Professor Kautz and his team have developed two photo manipulation and processing methods (Exposure Fusion and local Laplacian filtering) that are used to produce well-exposed photographs with tuneable local contrast. Both are robust and consistent without requiring any per-image parameter tuning. Due to its reliability and effectiveness, Exposure Fusion is now considered the standard method for blending multiple photographs into a single well-exposed photograph, and is used by a large number of commercial and non-commercial products. Local Laplacian filtering was chosen by Adobe Systems Incorporated to be the default tool for image enhancements in Adobe Lightroom and Adobe Camera Raw. As a result, these methods are now in the hands of hundreds of thousands users, who use them to create and manipulate well-exposed digital photographs.
This Keele University research in multiscale object recognition has led to two key breakthroughs: (a) the automated identification of tissue boundaries in computer tomographic (CT) scans, enabling the latest radiotherapy equipment to more accurately target diseased tissue thus avoiding neighbouring healthy organs. Such improvements are essential to the successful roll-out of new more precise linear accelerators in the treatment of cancer; (b) new fractal algorithms to characterise the quality of transplanted cell growth from post-operative biopsies. By automating the selection of the healthiest cells this has assisted the generation of patient-specific cartilage and is essential for the development of a medical capability for large-scale patient-specific generation of cartilage growth for the treatment of arthritis. It has indirectly led to software improvements in cell-tracking and to achieving reliable auto-focussing in high throughput non-invasive microscopy.
A collaborative research project between the Division of Imaging Sciences and Biomedical Engineering, King's College London (KCL) and Philips Healthcare has devised methods to register (i.e. align or match) pre-operative 3D computed tomography (CT) images to intraoperative 2D X-ray images, resulting in more accurate and robust registration/alignment measures. The measures can be applied directly to images from standard X-ray machines, allowing for rapid translation to guide surgical procedures and radiotherapy. These measures (or close variants) are used routinely in commercial products by Accuray, Philips Healthcare and Cydar Ltd (KCL spinout), benefitting the care of hundreds of patients worldwide, every day.
Our research on Active Shape Models (ASMs) and Active Appearance Models (AAMs) opened up a radically new approach to automated image interpretation, with applications in industrial inspection, medical image analysis, and face tracking/recognition. We identify:
Phase unwrapping is an essential algorithmic step in any measurement system or sensor that seeks to determine continuous phase. Instances of such devices are widespread: e.g. image reconstruction in magnetic resonance imaging (MRI), synthetic aperture radar (SAR) by satellite systems, analysis of seismic data in geophysics and optical instrumentation, to name but a few. Without successfully solving the phase unwrapping problem these instruments cannot function.
The topic is well developed and competition among algorithms is fierce. In 2012 alone, some 235 papers, most of which were describing potential new algorithms, were published in the area. But the continuing need for high-speed, automated and robust unwrapping algorithms poses a major limitation on the employability of phase measuring systems.
Working originally within the context of structured light 3D measurement systems, our research has developed new phase image unwrapping algorithms that constitute significance advances in speed, automation and robustness. The work has led to adoption by industry, as well as use in commercial and government research centres around the globe. Our approach since 2010 has been to make these algorithms freely available to end users. Third parties have gone on to translate our algorithms into other languages, widely used numerical software libraries have incorporated the algorithms and there are high profile industrial users.
The FLAIR (Fluid Attenuated Inversion Recovery) MRI sequence developed at Imperial College has transformed the sensitivity of clinical neuroimaging for white matter brain lesions. FLAIR has had significant commercial impact with incorporation as a standard imaging sequence offered by all manufacturers on their MRI scanners. The inclusion of FLAIR in routine diagnostic MRI protocols in radiology centres worldwide provides evidence of the continued extensive reach of impact for better healthcare outcomes through improved diagnosis and management. The use of FLAIR has led to more powerful Phase II trial designs for development of medicine for stroke, neuroinflammatory disorders, epilepsy and neuro-oncology based on imaging outcomes.