Chapter

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

Volume 7766 of the series Lecture Notes in Computer Science pp 215-224

Automatic Event Detection within Thrombus Formation Based on Integer Programming

  • Loic PeterAffiliated withCarnegie Mellon UniversityComputer Aided Medical Procedures, Technische Universitaet Muenchen
  • , Olivier PaulyAffiliated withCarnegie Mellon UniversityComputer Aided Medical Procedures, Technische Universitaet MuenchenInstitute of Biomathematics and Biometry, Helmholtz Zentrum Muenchen
  • , Sjoert B. G. JansenAffiliated withCarnegie Mellon UniversityDepartment of Haematology, University of CambridgeNational Health Service Blood and Transplant
  • , Peter A. SmethurstAffiliated withCarnegie Mellon UniversityDepartment of Haematology, University of CambridgeNational Health Service Blood and Transplant
  • , Willem H. OuwehandAffiliated withCarnegie Mellon UniversityDepartment of Haematology, University of CambridgeNational Health Service Blood and TransplantThe Wellcome Trust Sanger Institute
  • , Nassir NavabAffiliated withCarnegie Mellon UniversityComputer Aided Medical Procedures, Technische Universitaet Muenchen

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Abstract

After a blood vessel injury, blood platelets progressively aggregate on the damaged site to stop the resulting blood loss. This natural mechanism called thrombosis can however be prone to malfunctions and lead to the complete obstruction of the blood vessel. Thrombosis disorders play a crucial role in coronary artery diseases and the identification of genetic risk predispositions would therefore considerably help their diagnosis and therapy. In vitro experiments are conducted in this purpose by perfusing blood from several donors over a surface of collagen fibres, which results in the progressive attachment of platelets. Based on the segmentation over time of these aggregates called thrombi, we propose in this paper an automatic method combining tracking and event detection which allows the extraction of characteristics of interest for each thrombus growth individually, in order to find a potential correlation between these growth features and blood donors genetic disorders. We demonstrate the benefits of our approach and the accuracy of its results through an experimental validation.

Keywords

Microscopy image analysis thrombus segmentation multi-target tracking event detection