Abstract-Shape models used for the segmentation of 3D image data often suffer from high instability of shape. Current approaches to avoid this instability often result in models with high computation times and few possibilities for interaction and modelling.We present a 3D mass-spring model which has been extended by torsion forces and the capability of explicit rotation. These models are stable with respect to shape collapse and contortion. Stability is achieved even if the model is only sparsely connected. This makes the computation efficient enough for real-time interaction.The extended model has been successfully applied to the segmentation of the left ventricle of the human heart in 3D SPECT data.
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. nding the LV in the dataset, 2. extracting its approximate shape and 3. segmenting its exact contour. Finding of the LV is done by exible 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.
The presented technique is both, fast and flexible. It can be used to interactively derive automatic distance measures for arbitrary mesh-based segmentations. Due to the geometrically exact measurements, it is possible to reliably estimate safety margins, assess possible infiltrations and other clinically relevant measures. To exploit this benefit, the method requires precise segmentations as input data.
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