Automatic segmentation of the left ventricle in 3d SPECT data by registration with a dynamic anatomic model

Authors:
Lars Dornheim;Klaus D. Tönnies;Kat Dixon
Affiliations:
Institut für Simulation und Graphik, Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Germany;Institut für Simulation und Graphik, Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Germany;Medical Imaging Research Group, University of British Columbia, Canada
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

We present a fully automatic 3D segmentation method for the left ventricle (LV) in human myocardial perfusion SPECT data. This model-based approach consists of 3 phases: 1. finding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour. Finding of the LV is done by flexible pattern matching, whereas segmentation is achieved by registering an anatomical model to the functional data. This model is a new kind of stable 3D mass spring model using direction-weighted 3D contour sensors. Our approach is much faster than manual segmention, which is standard in this application up to now. By testing it on 41 LV SPECT datasets of mostly pathological data, we could show, that it is very robust and its results are comparable with those made by human experts.