Key result
A novel bottom-up 3D+time left ventricular segmentation technique required significantly less training than state-of-the-art methods while maintaining or improving segmentation accuracy.
A novel bottom-up 3D+time segmentation technique for cardiac MRI reduces training requirements while maintaining or improving accuracy compared to existing methods.
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.
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O'Brien et al. (2010) studied this question. Bottom-up modelling approach with flexible contour coupling vs. State-of-the-art model-based methods was evaluated on Segmentation accuracy and training requirements. A novel bottom-up 3D+time left ventricular segmentation technique required significantly less training than state-of-the-art methods while maintaining or improving segmentation accuracy.
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