TY - GEN
T1 - A learning-based approach to evaluate registration success
AU - Vetter, Christoph
AU - Kamen, Ali
AU - Khurd, Parmeshwar
AU - Westermann, Rüdiger
PY - 2010
Y1 - 2010
N2 - Clinical trials are more and more relying on medical imaging technologies to quantify changes over time during longitudinal studies. This calls for having an unsupervised batch registration process. However, even good registration algorithms fail, whether that is because of a small capture range, local optima, or because the registration finds an optimum that is not meaningful since the input data contains different anatomical sites. We propose a new method to evaluate the success or failure of batch registrations, so that failed or suspicious registrations can be flagged and manually corrected. The evaluation is based on a support vector machine that evaluates features representing the "goodness" of the registration result. We devise the features to be the distance measured between optima produced by different similarity measures as well as optima resulting from registering subsections of the volumes. The features of 30 volume registrations have been labeled manually and used for the learning phase. Based on a test on unseen 67 volume pairs of varying anatomical sites, we are able to classify 90% of the registrations correctly.
AB - Clinical trials are more and more relying on medical imaging technologies to quantify changes over time during longitudinal studies. This calls for having an unsupervised batch registration process. However, even good registration algorithms fail, whether that is because of a small capture range, local optima, or because the registration finds an optimum that is not meaningful since the input data contains different anatomical sites. We propose a new method to evaluate the success or failure of batch registrations, so that failed or suspicious registrations can be flagged and manually corrected. The evaluation is based on a support vector machine that evaluates features representing the "goodness" of the registration result. We devise the features to be the distance measured between optima produced by different similarity measures as well as optima resulting from registering subsections of the volumes. The features of 30 volume registrations have been labeled manually and used for the learning phase. Based on a test on unseen 67 volume pairs of varying anatomical sites, we are able to classify 90% of the registrations correctly.
UR - http://www.scopus.com/inward/record.url?scp=78049434703&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15699-1_45
DO - 10.1007/978-3-642-15699-1_45
M3 - Conference contribution
AN - SCOPUS:78049434703
SN - 3642156983
SN - 9783642156984
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 429
EP - 437
BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Y2 - 19 September 2010 through 20 September 2010
ER -