A learning-based approach to evaluate registration success

Christoph Vetter, Ali Kamen, Parmeshwar Khurd, Rüdiger Westermann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
TitelMedical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
Seiten429-437
Seitenumfang9
DOIs
PublikationsstatusVeröffentlicht - 2010
Veranstaltung5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 - Beijing, China
Dauer: 19 Sept. 201020 Sept. 2010

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band6326 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Land/GebietChina
OrtBeijing
Zeitraum19/09/1020/09/10

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