Click on a theme or a project in the table below for more information.
Project leader:
Dr. Peter Lucas (RUN)
Consortium:
RUN, UMC Radboud
Industrial partners (non-exhaustive):
Preventicon
Total FTE: 2.5 (heads: faculty: 2, PD: 2, PhD: 0.5)
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Project IS8: B-SCREEN: Bayesian Decision Support in Medical Screening
In 2006 digitization of the Dutch breast cancer screening started.
All screening mammograms will be stored in one national archive,
which will be facilitated by the use of broadband technology. As a
consequence, a large database of breast cancer cases will become
available in a few years. This provides a unique opportunity for
the development of decision-support systems in this domain. A
major cause of missing breast cancer cases is interpretation
failure. There is strong evidence that interpretation failure is a
more common cause of missing significant lesions in screening than
perceptual oversights. From audits it is known that in the
Netherlands more than 25% of all cancers detected in the screened
population show relatively clear signs of abnormality on previous
screening mammograms, while another 25% show minimal signs. The aim
of this project is to use Bayesian networks and Bayesian
classifiers to further address the problem of interpretation
failures by radiologists.
Breast cancer screening and CAD
There is evidence that computer-assisted detection (CAD) of lesions
in mammograms can be of help to radiologists in interpreting
whether a lesion is malignant or benign. However, interpretation of
lesions requires more medical background knowledge than is
currently taken into account in CAD systems. This problem is
addressed by a tight collaboration between radiologists and
computer scientists.
Image analysis of mammograms
This subproject focuses on improving feature extraction in
mammograms. Detection of breast cancer in mammograms can be
modelled as a multi-stage process. In a first step a search is
carried out to identify locations of interest in the images.
Sensitive methods for automating this step have been developed in
the past and will be used in this project. These methods comprise
detection of masses, microcalcifications, architectural distortion,
and asymmetry, which are all signs of breast cancer. There is a
need for the further development of image feature extraction and
standardised data representation based on classification of local
image features in single views. By combining information extracted
from different views, we hope to be able to improve interpretation
of mammograms.
Learning Bayesian networks from data
This subproject focuses on the development of methods that allow
incorporation of radiological background knowledge in constructing
Bayesian networks. Background knowledge is expected to play a role
both in the elucidation of the appropriate Bayesian network
topology as in finding appropriate context-specific dependence
information. Relational probabilistic models and similarity
networks have been chosen so far as a starting point for this line
of research.
Observer Studies and Estimation of the Value of CAD for Radiologists
In this subproject we aim to obtain insight into the nature of the
task of detection of breast lesions, suspected of cancer. We have
established a good working relationship with Preventicon (breast
cancer screening foundation in Utrecht, the Netherlands) and are
now collaborating with radiologists in findings out which features
and combinations of features, when detected, may help radiologists
in reducing the misinterpretation error.
Industrial cooperation
There is significant interest among medical imaging companies for CAD.
Highlights
As this project is part of the third phase of the BRICKS program
(financed through the second open round July 2006), challenges
rather than results are presented.
Research challenges
- Understanding the task of tumour mass detection in mammograms.
- Design of a computer-based language for the representation of radiological background knowledge, which will act as the basis for the estimation of joint probability distributions, taking into account background knowledge of experts.
- Improvement of classification performance of computer-aided detection software using Bayesian networks.
Economic & societal impact
Improvement of CAD of lesions in mammograms may save lives, and successful completion of the project may therefore have significant impact. International cooperation There is collaboration with the European community in probabilistic graphical models (in particular with research groups in Spain) and the CAD community.
Future work 2007-2009
The project has just started at the end of 2006 and arrangements
for a close collaboration between the project partners are now in
place. Each of the research challenges mentioned above will be
addressed in the coming years.
IS8 Researchers funded by BRICKS
- Dr.ir. N. Karssemeijer (RU)
- Dr. P.J.F. Lucas (RU)
- Dr. N. De Carvalho Ferreira (RU)
- Dr. M. Velikova (RUMC)
- Dr. M. Samulski (RUMC)
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