Definiens in Medical Imaging

Understanding Images
DEEPER INSIGHTS
FASTER RESULTS
BETTER DECISIONS
Definiens in
Medical Imaging

2
The Challenge in
Medical Imaging
Medical imaging helps to detect, identify and observe tumors
over time. Screening, for example, is the first line of defense
against breast cancer. CT and MRI scans are vital diagnostic
tools. Biopsies help to determine whether lesions are malig-
nant or benign. And, once treatment has begun, medical
imaging is one of the most powerful tools for measuring
therapeutic response.
The proliferation of image data in oncology has created some major challenges.
First and foremost, there is a global shortage of trained radiologists and histopathologists.
Without them, the increasing number of scans and tissue samples cannot
be analyzed, creating an ‘image analysis bottleneck’ that delays the diagnosis and
treatment of patients.
Secondly, the task is very complex. Due to the inherent variability of living organisms,
even the most detailed scans can be difficult to interpret. As we strive to detect a
cancer ever earlier, many tumors are so small and escape easy detection. The Journal
for Thoracic Imaging noted, in a study evaluating the detection of small pulmonary
nodules, that: “Observer detection of pulmonary nodules in the range of 5-15mm
using current digital radiography systems is not reliable in the confusing environment
of the lung.” 1
Spotting lesions and tumors requires concentration, training and a very good eye. In
this challenging interpretive environment, human error is compounded by fatigue
and an overload of data.
Taken together, these challenges delay detection, diagnosis and treatment.This causes
unnecessary patient suffering and results in more costly and less effective treatment.
Screening Diagnostic Staging Therapy Follow-up
e.g. Mammography e.g. CT Colonoscopy e.g. Mammography e.g. CT Colonoscopy e.g. Whole Body MR
Figure 1 Patient diagnostic and treatment paradigm
Early diagnosis starts with screening, followed by diagnostic tests if suspicious lesions are found. In the case of malignant
findings the staging phase will be used to size and locate tumors which may be treated with surgical, chemical or radiation
therapy or a combination. Following the treatment a “re-staging” evaluates the success of therapy.
Definiens in Medical Imaging
1 Wu, N, et al.Vol. 21 (1): pp27-31. March 2006. Available at: www.ncbi.nlm.nih.gov/pubmed/16538152.

Medical Imaging
in Oncology
Definiens’ software can be used to complement or improve any existing imaging
system. It is easily incorporated into current systems to provide an enhanced level of
image intelligence. This enables medical professionals to detect cancer earlier and
supports them in making the best decisions to provide personalized treatment for
patients.
The benefits in oncology
Definiens’ technology is ideally suited to screen, identify, diagnose and treat cancer
in four ways. The technology increases sensitivity, improves consistency, increases
productivity, and accurately measures the volume of tumors helping to assess the
efficacy of treatment.
As a result, Definiens’ technology helps clinicians and specialists to identify tumors
earlier, make better treatment decisions and assess the efficacy of treatment
more accurately. The cost of treatment is reduced and patients benefit from earlier
intervention and improved treatment.
Example: Automatic segmentation of the liver
In 20 datasets, representing a large variation of pathological cases, the liver was
automatically segmented. In each case, the resulting segmentation was evaluated
using the manual annotation generated jointly by two medical experts.
Definiens’ technology achieved an average volumetric error of 7.6% and an average
mean surface distance of 2.73mm. In view of the short prototype development time
(less than 30 days) the performance was good and several issues were observed for
future development.
A potential source of error is lesions, which may be initially misclassified as air or fat
and therefore not subject to the “dark object inclusion” process. Both these issues have
been addressed in a later version of the software.
The potential of Definiens Cognition
Network Technology® to deliver
auto mated, precise and robust 3D
detection of liver was demonstrated
even in pathological cases. The
abi li ty to incorporate explicit domain
knowledge and context in a
simple form is unique and, in our
opinion, a mandatory feature for the
development of complex solutions.
These solutions relieve radiologists of
many mundane image analysis tasks
and enable them to focus precious
time on the diagnosis and treatment
of patients.
Figure 2 Screenshot showing analysis of 20 Liver Samples. a) Directory with 20 images, b) Transversal 2D display,
detected liver segments outlined in red, c) Process hierarchy of anatomical model, d) 3D visualization of segmented liver,
e) Information on selected liver image
Definiens in Medical Imaging 3

4
Definiens in Medical Imaging
Computer Aided
Detection
“Automatic volumetry and segmentation allows reliable detection
of tumor growth and has the potential to increase
reliability and significance of monitoring growth in follow-up
examinations.” 2
International Journal of Computer-Assisted Radiology and Surgery
Studies have shown that CAD systems aim to identify and volumetrically measure
different types of tumors and polyps. They have proven to be as accurate as many
trained experts.
In a colonography study reported in the American Journal of Roentgenology, experts
concluded that a CAD system’s standalone performance exceeds human standards,
and that it should be used synergistically with experts. 3
Volumetric measurement
Volumetric measurement is by far the most accurate way of measuring tumor size
which, in turn, is a key indicator of whether the disease is responding to treatment,
stabilizing or progressing.
A study in The Journal of Clinical Oncology compared RECIST predictions to accurate
volumetric measurements. RECIST correctly predicted volumetric response or progression
in 12 out of 17 cases; and in 3 of these 12 cases it needed one or two additional
scan cycles.
The delay identifying cases where the tumor was progressing was particularly
noticeable: RECIST was only 50 per cent specific for progressive disease at the
time that the progression was documented with volume. This means that 8 out of
17 cases (just under 50 per cent) were not assessed as quickly as they might have been. 4
Such delays may be vital to a patient’s chances of survival.
Figure 3 Automatically detected and classified organs and lymph nodes in an axial view CT image
2 ‘OncoTREAT: a software assistant for cancer therapy monitoring’. Borneman, Lars, et al. Vol. 1 (5): pp231-242 (February 2007). Available at: www.springerlink.
com/content/1518220748810553/.
3 ‘Computer-Assisted Reader Software Versus Expert Reviewers for Polyp Detection On CT Colonography.’
4 Jaffe, Carl C.Vol. 24 (20): pp3245-3251. July 10 2006. Available at: http://jco.ascopubs.org/cgi/content/abstract/ 24/20/3245

Information
Centric Healthcare
One of the most powerful tools in supporting decision making
is the comparison of a case with a database of similar disease
and patient patterns. This database contains consolidated
multi-modal diagnostic tests paired with patient information
(like risk profile, disease history, genetic profile) and thus
serves as a knowledge base.
Definiens’ technology offers image analysis solutions that deliver accurate and
consistent results. It extracts objects and creates hierarchical levels of classifi cation
representing networked objects of interest on different scales.
A wide variety of decisive morphological parameters can be measured and exported
including spectral statistics, shape, size, position, texture and relations to neighbor
objects, sub objects and super objects.
The scaleable platform can handle image analysis tasks of any size, quickly, accurately
and consistently.
In pre-clinical studies where Definiens’ technology has been deployed, it has saved
between 60-90 % of the project’s image analysis costs. That is without considering
the vast savings made where problems are identified early and a drug never enters
clinical trials.
Full-Size Size DICOM, four Views Detection of calcifications and masses, export
image object statistics and patient metadata to CSV
Patient search in Definiens Developer Image object statistics (Exel)
Figure 4 Processing workflow for analysis of multimodal mammography images and tables.
Definiens in Medical Imaging 5

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Definiens in Medical Imaging
Example: Case-based retrieval in mammography
Definiens developed a prototype which extracts breast, nipples, calcifi cations and
masses from x-ray mammograms. The statistical results can be used for a similarity
search in a case database.
The image data is analyzed via the following steps:
1. Accurate segmentation of object of interest
2. Detection and classification of masses and calcifications and creation of typical
fingerprint including lesion morphology
3. Recording the results in a table
4. Comparison of the actual case with similar cases in the table
5. Presentation of similar cases
The Results
The prototype was applied to 28 cancer cases. In 6 cases, clustered calcifications were
the only indications of cancer. In 8 cases malignant masses indicated cancer. The
remaining 14 cases had calcifications within masses.
The Definiens‘ approach detected at least one cancer region in 92.9 % of the cancer
cases.Where there were malignant calcification clusters, the algorithm detected at least
one cluster in all cases. The detection rate for cancer cases that contained malignant
masses was 90.9 %.
In a multi-modal approach, images from different image modalities are analyzed
in parallel. The corresponding prototype can link tumor objects to corresponding
objects in images of the same or a different modality. Representative statistical results,
for example on tumor morphology can be exported to support an expert’s diagnosis.
Conclusions
The results on different data modalities performing Computer Aided Detection of
breast lesions look very promising. The technology will be the basis for the creation
of new generation of CAD Systems, not only for the breast, but also for other organs by
using different patient-relevant data in parallel.
Figure 5 Screenshot of linked image information, with
a1) US-, a2) x-ray- and a3) MR-mammography, analyzed
by using a CNL prototype, which consists of a process
hierarchy b1) and a class hierarchy b2). Tumors are linked
to each other (outlined in red)
Acknowledgment
This work was supported by the Bayerische For schungsstiftung
within the scope of the project Mammo-iCAD.

Better Decisions Faster Results Deeper Insights
The Definiens Edge
Definiens Cognition Network Technology® surpasses every other
automated image analysis system today across nine different criteria
delivering deeper insights, faster results and better decisions.
Definiens handles complex real world situations.
The multi-scale object network can explore an image, the objects in it and the relations between
them simultaneously on multiple levels. The technology can even detect anomalies like abnormal
cells in a tumor.
Definiens supports all common types of imaging systems.
Definiens’ technology interprets all types and combinations of digital image data. This includes all
the major automated microscopes, digital slide scanners and non-invasive imaging scanners in the
life sciences.
Definiens provides detailed quantification of images.
Definiens’ technology provides users with detailed morphometric quantification of any type of
images for any type of task, reducing or eliminating tedious manual analysis. The extracted data can
be correlated against molecular data, significantly increasing the level of insight.
Definiens’ applications can be developed in a fast and modular way.
Applications can be developed rapidly by combining existing ruleware modules. Complex tasks can
be addressed with the full power of Definiens Cognition Network Technology®, which in turn creates
ruleware that can easily be reused in other situations.
Definiens’ applications identify the areas and structures of interest reliably.
Definiens’ technology can extract any kind of structures in image data – even under difficult
circumstances and in difficult cases. This is the key to its success in analyzing and providing detailed
quantification of image content.
Definiens enables fully automated information extraction.
Definiens’ technology offers completely automated image analysis. Its user-friendly interfaces assist
scientists and bio-informaticians to extract information efficiently. Definiens provides the perfect
platform for high throughput analysis of image data.
Definiens provides reliable information for decision making.
The deterministic approach of Definiens’ technology ensures that all results are transparent and fully
reproducible. The results can be presented in the format best suited for the decision making process:
as labeled regions in the image, as objects saved in a database or as statistical summaries.
Definiens integrates image intelligence across existing processes.
Definiens’ technology is highly scalable, features a modular design and integrates easily into any
environment. Its distributed architecture enables it to be deployed across the entire enterprise,
supporting end-to-end decision making processes and helping to realize the vision of translational
medicine.
Definiens will continue to lead the field.
The unique patented software, built on an open standards-based architecture, is a new paradigm in
automated image analysis. Gerd Binnig and his team continue to lead the research and development at
Definiens, ensuring that the technology will remain at the forefront of enterprise image intelligence.
Definiens in Medical Imaging 7

DEEPER INSIGHTS
FASTER RESULTS
Understanding Images BETTER DECISIONS
Definiens is the number one Enterprise Image Intelligence company for analyzing and interpreting images
on every scale, from microscopic cell structures to satellite images.
The patented Definiens Cognition Network Technology®, developed by Nobel Laureate Prof. Gerd Binnig
and his team, emulates human cognitive processes of perception to extract intelligence from images.
If you are interested in learning more about how Definiens could address the challenges you face, please
visit our website.
www.definiens.com
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