Automatic event detection within thrombus formation based on integer programming

Authors:
Loic Peter;Olivier Pauly;Sjoert B. G. Jansen;Peter A. Smethurst;Willem H. Ouwehand;Nassir Navab
Affiliations:
Computer Aided Medical Procedures, Technische Universitaet Muenchen, Munich, Germany;Computer Aided Medical Procedures, Technische Universitaet Muenchen, Munich, Germany, Institute of Biomathematics and Biometry, Helmholtz Zentrum Muenchen, Munich, Germany;Department of Haematology, University of Cambridge, Cambridge, United Kingdom, National Health Service Blood and Transplant, Cambridge, United Kingdom;Department of Haematology, University of Cambridge, Cambridge, United Kingdom, National Health Service Blood and Transplant, Cambridge, United Kingdom;Department of Haematology, University of Cambridge, Cambridge, United Kingdom, National Health Service Blood and Transplant, Cambridge, United Kingdom, The Wellcome Trust Sanger Institute, Hinxton ...;Computer Aided Medical Procedures, Technische Universitaet Muenchen, Munich, Germany
Venue:
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Year:
2012

Citing 4
Cited 0

MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Target Tracking - Linking Identities using Bayesian Network Inference

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Cell population tracking and lineage construction with spatiotemporal context

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

Foundations and Trends® in Computer Graphics and Vision

Quantified Score

Hi-index	0.00

Visualization

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.