B-SCREEN

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[edit] General project information

  • Title: Bayesian Decision Support in Medical Screening
  • Acronym : B-SCREEN
  • Time frame: 2006 - 2009
  • Funded by NWO under BRICKS/FOCUS grant number 642.066.605

[edit] Project outline

[edit] Objectives

In 2006 digitisation of the Dutch breast cancer screening has 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 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.

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. The aim of this project is to use Bayesian networks and Bayesian classifiers to further address the problem of interpretation failures by radiologists. 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.

  • Understanding the task of tumour mass detection in mammograms.
  • Design of a computer-based language for the representation of radiological background knowledge.
  • Improvement of classification performance of computer-aided detection software using Bayesian networks.

[edit] Methodology

[edit] 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.

[edit] 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.

[edit] 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 radiologist in reducing the misinterpretation error.

[edit] Project partners and collaborations

[edit] Researchers

  • Dr. Peter Lucas, MD (Principal Investigator), Institute for Computer and Information Science, Radboud University Nijmegen.
  • Dr. Ir. Nico Karssemeijer (Principal Investigator), Department of Radiology, UMC St. Radboud Nijmegen.
  • Dr. Marina Velikova, Institute for Computing and Information Sciences, Radboud University Nijmegen.
  • Dr. Nivea de Carvalho Ferreira, Institute for Computer and Information Science, Radboud University Nijmegen.
  • Maurice Samulski MSc, Department of Radiology, UMC St. Radboud Nijmegen.
  • Dr. Carla Boetes, Department of Radiology, UMC St. Radboud Nijmegen.

[edit] Publications / Presentations

  • M. Velikova, M. Samulski, N.Karssemeijer, P. Lucas, Toward expert knowledge representation for automatic breast cancer detection, In Proceedings of the 13th biennial International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA), LNAI 5253, pp. 333-344, 2008. [PDF]
  • M. Velikova, H. Daniels, M. Samulski, Partially monotone networks applied to breast cancer detection on mammograms, In Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN), LNCS 5163, pp. 917-926, 2008. [PDF]
  • M. Velikova, P. Lucas, N. de Carvalho Ferreira, M. Samulski, N.Karssemeijer, A decision support system for breast cancer detection in screening programs, In Proceedings of the 18th biennial European Conference on Artificial Intelligence (ECAI), Vol. 178, 2008.
  • N. de Carvalho Ferreira, M.Velikova, P. Lucas, Bayesian Modelling of Multi-View Mammography, Presented at the ICML workshop "Machine Learning for Health Care Applications", Helsinki, Finland, July 2008. [PDF]
  • M. Samulski and N. Karssemeijer. Matching mammographic regions in mediolateral oblique and cranio caudal views: A probabilistic approach. In Proceedings of SPIE, Vol. 6915, Medical Imaging 2008: Computer-Aided Diagnosis, Maryellen L. Giger, Nico Karssemeijer, Editors, ISBN: 9780819470997, San Diego, CA, 2008. [PS] [PDF]
  • M. Velikova, N. de Carvalho Ferreira, P. Lucas, Bayesian network decomposition for modeling breast cancer detection, Proceedings of the 11th Conference on Artificial Intelligence in Medicine (AIME) 2007, LNA 4594, pp. 346-350, 2007.
  • M. Samulski, N. Karssemeijer, P. Lucas M.D., P. Groot, Classification of mammographic masses using support vector machines and Bayesian networks, Proceedings of SPIE, Vol. 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 2007.

For more publications go to Publications or to the personal web-pages of the project researchers.

[edit] In Press

[edit] Additional information

Under work on B-SCREEN you can find information about project meetings, reports, related work and demos/software updates.

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