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:
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).
SIAscopy is an image analysis method using the physics of image
formation. It non-invasively provides near-instant quantitative maps of
the key histological components of the skin. The scientific underpinnings
were developed by Prof. Claridge's group, patented, and commercialised via
a spin-off company Astron Clinica. SIAscopy was incorporated into
medical imaging products which improved accuracy of general practitioners
in diagnosis of melanoma, a skin cancer, whilst delivering higher
cost-effectiveness than best clinical practice. Developed primarily for
cancer diagnosis, SIAscopy also found uses in the cosmetics industry. In
2011 the current IPR owner, MedX, estimated the US market opportunity for
the technology to be around $1 Billion.
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.
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,
Atrial fibrillation (AF), a form of cardiac rhythm disturbance,
significantly increases risk of stroke, heart failure and sudden death.
The Division of Imaging Sciences and Biomedical Engineering at King's
College London and Philips Healthcare collaborated to develop a platform
for guiding cardiovascular catheterisation procedures in patients with AF.
The EP Navigator is a commercial, clinical product that integrates
pre-acquired magnetic resonance and computer tomography images with
real-time X-ray fluoroscopy. This enhances visualisation, thereby reducing
procedure time and the patient's exposure to radiation. The EP Navigator
is used in around 350 out of 2,000 centres worldwide that carry out
ablation therapies for cardiac arrhythmias, despite strong competition.
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.
Professor Alexander's work on diffusion magnetic resonance imaging (MRI)
modelling and processing has had significant and lasting impact on medical
practice. In particular, neurosurgical support systems rely on his work to
map the major connection pathways in the brain, helping the surgeons avoid
damaging them during intervention. Specific examples are in epilepsy,
where, since 2010, surgeons perform about one operation per week using
these systems, and brain tumour resection, where surgeons in Milan have
since early 2013 been using a similar system based on UCL's latest
microstructure imaging techniques. The key impact is on patients, whose
likelihood of permanent post-operative deficits in, for example, visual,
verbal or motor skills, is significantly reduced.
This case study describes how computational research in boundary problems
at Middlesex was applied to bio-imaging using Electrical Impedance
Tomography (EIT) for imaging brain function, lung function and tumour
detection, and the development of Optical Tomography of brain function in
neonates. This has resulted in the contribution of several public domain,
open source resources to the international industrial and commercial
research communities such as novel reconstruction algorithms, geometric
models for generating accurate finite element and boundary element forward
models and methods to generate subject-specific forward models which have
been exploited as detailed below. It has also resulted in two patents.
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.