Case Study 4: Quantitative Image Analysis – Novel Biomarkers for Clinical Trials and Diagnostics (IXICO)
Submitting InstitutionImperial College London
Unit of AssessmentComputer Science and Informatics
Summary Impact TypeTechnological
Research Subject Area(s)
Information and Computing Sciences: Artificial Intelligence and Image Processing
Medical and Health Sciences: Neurosciences
Summary of the impact
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 development of a spin-off company, IXICO, which has licenced the
developed image analysis techniques and imaging biomarkers.
- The use of the image analysis techniques and imaging biomarkers in
more than 40 clinical trials involving more than 10000 subject visits.
- The approval of imaging biomarkers by European regulators as a tool to
enrich recruitment into regulated clinical trials in Alzheimer's disease
The underpinning research has been carried out in the Biomedical Image
Analysis (BioMedIA) Group between 1999 and now. Professor Daniel Rueckert
founded the group in 1999 when he moved to Imperial College and has been
leading the research described below.
Much of the early work of the group has focused addressing one of the
fundamental problems in computer vision and medical image computing,
namely the problem of image registration. The goal of image
registration is to find automatically a transformation between points in
two or more images. For the transformation to be meaningful, the
transformation must map corresponding points across the coordinate
systems. If the transformation sought is rigid, the problem
is relatively straightforward to solve, as the number of unknowns is small
(typically 3 in 2D and 6 in 3D). However, when the transformation sought
is non-rigid the problem is much harder to solve since the
degree of freedom is much higher (typically in the order of hundreds of
thousands or even millions). Yet in practice non-rigid registration is
often required to compensate for intra-subject variation (for example
tissue deformation, respiratory or cardiac motion) as well as for
In 2001, Professor Rueckert and his team developed a solution to this
problem that is based on a flexible and versatile deformation model using
B-spline free-form deformations . Moreover, this approach is capable of
registering mono-modal and multi-modal images. Furthermore, the solution
developed by us was the first one to adopt the use of adaptive,
hierarchical B-spline free-form deformations that offer the ability to
deal with complex deformations. This proposed solution has been widely
adopted; in a recent comparison study it also has been shown to be amongst
the most accurate solutions for this problem . In 2006, they proposed
an improved solution to the registration problem that uses a diffeomorphic
deformation to allow the modelling of very large deformations that may
occur when registering the images of different subjects .
The ability of the developed image registration techniques to deal with
very large deformations has led us to develop novel solutions to the
classical problem of image segmentation that are based on image
registration. As part of the EPSRC-funded IXI [i] and IBIM [ii] projects,
we have pioneered the use of non-rigid registration of multiple atlases
followed by vote or label fusion for the automatic segmentation of images
. Standard atlas-based segmentation uses image registration to transfer
anatomical information from an atlas to new, unseen images. In contrast to
this, our multi-atlas segmentation  uses multiple atlases and
registrations followed by machine learning approaches such as decision
fusion to provide a consensus estimate of the segmentation. This provides
a much more robust and accurate segmentation of medical images. This
approach has become the de-facto standard solution for medical image
segmentation in many applications, in particular for neurological,
abdominal, and cardiac images. The name of the research project in [i]
("IXI") has also inspired the name of the resulting spin-off company
("IXICO" — see section 4 for more details).
More recently, as part of the PredictAD FP7 project [iii] we have
further developed the methodology described above to enable the robust and
accurate extraction of several imaging biomarkers, in particular in the
context of neurodegenerative diseases such as dementia. For example, our
methodology allows the accurate measurement of hippocampal volume  and
hippocampal volume loss .
References to the research
Publications that directly describe the underpinning research
* References that best indicate quality of underpinning research.
 J. A. Schnabel, D. Rueckert, M. Quist, J. M. Blackall, A. D.
Castellano Smith, T. Hartkens, G. P. Penney, W. A. Hall, H. Liu, C. L.
Truwit, F. A. Gerritsen, D. L. G. Hill, and D. J. Hawkes. A generic
framework for non-rigid registration based on non-uniform multi-level
free-form deformations. In Fourth Int. Conf. on Medical Image Computing
and Computer-Assisted Intervention (MICCAI), 2208 pages 573-581, 2001.
 A. Klein, J. Andersson, B. A. Ardekani, J. Ashburner, B. Avants,
M.-C. Chiang, G. E. Christensen, D. L. Collins, J. Gee, P. Hellier, J. H.
Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren,
R. P. Woods, J. J. Mann, R. V. Parsey. Evaluation of 14 nonlinear
deformation algorithms applied to human brain MRI registration.
Neuroimage, 46(3):786-802, 2009.
 *D. Rueckert, P. Aljabar, R. Heckemann, J. Hajnal and A. Hammers.
Diffeomorphic Registration using B-Splines. In Ninth Int. Conf. on Medical
Image Computing and Computer-Assisted Intervention (MICCAI), 4191pages
 *R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert and A.
Hammers. Automatic anatomical brain MRI segmentation combining label
propagation and decision fusion. NeuroImage, 33(1):115-126, 2006
 *R. Wolz, P. Aljabar, J. V. Hajnal, A. Hammers, D. Rueckert. The
Alzheimer's Disease Neuroimaging Initiative 2. LEAP: Learning embeddings
for atlas propagation. NeuroImage, 49(2): 1316-1325, 2010 (Patent
filed US2012/281900 A1 and exclusively licensed to IXICO)
 R. Wolz, R. A. Heckemann, P. Aljabar, J. V. Hajnal, A. Hammers, J.
Lötjönen, D. Rueckert. The Alzheimer's Disease Neuroimaging Initiative 1.
Measurement of hippocampal atrophy using 4D graph-cut segmentation:
Application to ADNI. NeuroImage, 52(1): 109-118, 2010 (Patent filed
US2012/281900 A1 and exclusively licensed to IXICO)
Grants that directly funded the underpinning research
[i] Information eXtraction from Images (IXI). EPSRC GR/S21526/01. D.
Rueckert (Co-I). £544,143, October 2003 — December 2006.
[ii] IBIM — Integrated Brain Image Modelling, EPSRC GR/S82503/01, D.
Rueckert (Co-I). £786,327, October 2004 — December 2007.
[iii] PredictAD — From patient data to personalized healthcare in
Alzheimer's disease, EU FP7 STREP FP7-224328, D. Rueckert (PI). €531,786,
June 2008 — June 2011.
Details of the impact
The underpinning research has led to the development of novel imaging
biomarkers that are now routinely used in clinical trials to assess the
efficacy of new drugs and treatments. The biomarkers are also starting to
be used in healthcare diagnostics, e.g. for dementias such as Alzheimer's
To maximize the economic impact of the research, a team of Imperial
researchers (Rueckert, Hajnal) started IXICO with colleagues from UCL
(Hawkes, Hill) in late 2004. It became IXICO plc and is now listed on the
Alternative Investment Market (AIM) of the London Stock Exchange in a deal
agreed in April 2013. Between 2008 and 2013, IXICO has grown from 5
employees to more than 40 employees. Its revenues have more than trebled
during the last three years to £3.6M (year ending 31 May 2013). IXICO is a
profitable business and has won more than £17m in business from the global
pharmaceutical industry (GSK, Pfizer, Bristol-Myers Squibb, Novartis,
EliLily). IXICO's image analysis technology is based on the underpinning
research described in section 2 and has been transferred from Imperial
during IXICO's formation and later as part of an IP pipeline agreement
with Imperial. It has been, and/or is currently being used to analyse tens
of thousands of medical images collected from a total of more than 400
imaging centres across North America, Latin America, Europe, Asia and
Australasia, including 25 hospitals in 10 cities across China. Since 2008
IXICO has been involved in over 40 clinical trials and analysed images
from more than 10000 subject visits using its image analysis technology
The research also had a significant impact on the pharmaceutical industry
where medical imaging is rapidly becoming an important tool in clinical
trials to assess the safety and efficacy of new drugs using imaging
biomarkers. In clinical trials phase 1 is typically used to screen for
safety, phase 2 establishes the testing protocol, phase 3 is used for
final testing while post-approval studies are referred to as phase 4
trials. Clinical trials (especially phase III) are typically very
expensive. The average cost of developing new drugs can reach billions of
dollars for each one approved. According to a Deutsche Bank Market Report
(August 2012), the average cost of a new, approved drug has increased from
$100M in 1979 to $1.9B in 2011 [B]. Imaging can significantly reduce these
costs by enriching the enrolled population, providing early evidence of
target engagement, or evidence of disease modification by being more
precise than clinical measures. However, the detection and quantification
of these subtle changes requires highly accurate and sensitive imaging
biomarkers that are determined via automatic and quantitative image
analysis. Such imaging biomarkers have been developed in the underpinning
research, in particular for the assessment of neurodegenerative diseases
and their progression.
The developed imaging biomarkers provide several benefits to
pharmaceutical companies: In concept trials of AD therapies the developed
biomarkers allow pharmaceutical companies to power their studies with
fewer subjects. The developed imaging biomarkers do provide evidence of
efficacy with around 100 subjects per arm (an "arm" in a clinical trial
refers to any of the treatment groups in a randomized trial. Most
randomized trials have two "arms", e.g. untreated vs. treated groups) over
12 months rather than 400 or so per arm needed for cognitive testing.
Based on a conservative cost estimate of $30k per subject enrolled, this
provides a significant cost saving for the companies. Similarly,
pharmaceutical companies use the developed imaging biomarkers to enrich
their clinical trials. In the context of clinical trials, such enrichment
allows the identification of a population of patients in whom a drug
effect, if present, is more likely to be demonstrable. In AD trials that
use the developed biomarkers, an increase in the conversion rate in a
prodromal Alzheimer's trial from 40% to 60% saves 30% off the cost of a
pair of pivotal trials that used progression free survival as an endpoint,
and which might otherwise cost $800m — $1bn [B].
Impacts on public policy and services
The imaging biomarkers developed in the underpinning research have had a
significant impact on informing the development of new guidelines for the
use of Magnetic Resonance Imaging (MRI) and low hippocampal volume in
regulatory clinical trials: It now seems likely that to modify the course
of Alzheimer's Disease, it is necessary to start the treatment in the
pre-dementia (or prodromal) phase. As has been recently reported [C], the
identification of patients at this stage can only be done confidently with
the help of biomarkers: imaging provides a non-invasive alternative to
cerebrospinal fluid (CSF) biomarkers for this purpose. The critical
importance of imaging biomarkers in AD trials has been recognised by the
CAMD consortium by submitting to regulators an application to qualify low
hippocampal volume as a biomarker [D]. This submission — approved by EMA
and currently under review by the FDA — incorporates key data obtained
using the underpinning research described here: the availability of this
technology, with the regulatory qualification, is having global impact on
the design of future trials of AD medicines in the pre-dementia
population. In particular, the EMA Committee for Medicinal Products for
Human Use [E] has issued a positive opinion on the use of MRI to measure
hippocampal volume as a tool to enrich recruitment into regulated clinical
trials in the pre-dementia stages of Alzheimer's disease [E], in which the
EMA directly refers to reference  of the underpinning research. This
was the first imaging-based biomarker to be qualified by a regulatory
Impacts on healthcare
The imaging biomarkers developed in the underpinning research have been
so effective in clinical trials that IXICO has recently decided also to
develop products for diagnostic use (Brain Health Centre [F]).
IXICO's product for diagnostics directly uses the methods described in
reference [5,6] of the underpinning research and has also been CE marked
[G]. It is currently undergoing trials involving 200 patients as part of
new NHS brain health centres [F, H].
Sources to corroborate the impact
[A] CEO, IXICO to confirm details regarding IXICO.
[B] Deutsche Bank Markets Research Report on the European Pharmaceutical
Industry (August 2012) pg 11 and 29-30. Available at http://www.fullermoney.com/content/2012-08-30/PharmaforBeginners82912.pdf.
on 22/10/2013. Corroborates that the average cost of a new, approved drug
has increased from $100M in 1979 to $1.9B in 2011 .
[C] M. S. Albert, S. T. Dekosky, D. Dickson, B. Dubois, H. H. Feldman, N.
C. Fox, A. Gamst, D. M. Holtzman, W. J. Jagust, R. C. Petersen et al. The
diagnosis of mild cognitive impairment due to Alzheimer's disease:
Recommendations from the National Institute on Aging — Alzheimer's
Association workgroups on diagnostic guidelines for Alzheimer's disease.
Alzheimer's & Dementia, 7: 270-279, 2011. http://dx.doi.org/10.1016/j.jalz.2011.03.008
[D] Coalition Against Major Diseases (CAMD) — Critical Path Institute:
European Medicines Agency Deems Imaging Biomarker a Qualified Measure to
Select Patients with Early Stages of Cognitive Impairment for Alzheimer's
Disease Clinical Trials available at http://c-path.org/wp-content/uploads/2013/08/MRI.pdf.
[E] Qualification opinion of low hippocampal volume (atrophy) by MRI for
use in regulatory clinical trials — in pre-dementia stage of Alzheimer's
disease by the European Medicine Agency (EMA) available at
[F] Description of IXICO's Brain Health Centre available at http://www.thebrainhealthcentre.com/.
Archived on 22/10/2013 https://www.imperial.ac.uk/ref/webarchive/qyf
[G] Description of IXICO's diagnostic tools available at http://www.ixico.com/products/assessa.
Archived on 21/10/2013 https://www.imperial.ac.uk/ref/webarchive/xyf
[H] The fast-track dementia test: PM to announce creation of new NHS
hi-tech brain clinics, Daily Mail, November 2012. http://www.dailymail.co.uk/health/article-2227855/The-fast-track-dementia-test-PM-announce-creation-new-NHS-hi-tech-brain-clinics-help-cut-diagnosis-time-18-months-just-three.html.
Archived on 22/10/2013 https://www.imperial.ac.uk/ref/webarchive/vyf