A novel model-based 3D +time left ventricular segmentation technique.
A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D +time structures, such as the cardiac left ventricle, due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the "curse of dimensionality." Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epicardium contours of the left ventricle by coupling them at segmentation-time, instead of at model-time. We apply our approach to 33 3D +time cardiac MRI datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy.
Centre for Image Processing and Analysis, Dublin City University, Dublin 9, Ireland. stephen.obrien32@mail.dcu.ie
Stephen P
SP
O'Brien
Ovidiu
O
Ghita
Paul F
PF
Whelan
D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D006321:Heart / Q000033:anatomy & histology*; D006352:Heart Ventricles / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D012044:Regression Analysis; D016276:Ventricular Function / Q000502:physiology