Simultaneous Volumetric Segmentation of Vertebral Bodies and Intervertebral Discs on Fat-Water MR Images

Fat-water magnetic resonance (MR) images allow automated noninvasive analysis of morphological properties and fat fractions of vertebral bodies (VBs) and intervertebral discs (IVDs) that constitute an important part of human biomechanical systems. In this paper, we propose a fully automated approach for simultaneously segmenting multiple VBs and IVDs on fat-water MR images without prior localization or geometry estimation. This method involved a hierarchical random forest (HRF) classifier and a hierarchical conditional random field (HCRF) that encoded a multi-resolution image pyramid based on a set of multiscale local and contextual features. The HRF classifier employed penalized multivariate linear discriminants and SMOTEBagging to handle limited and imbalanced training data with large feature dimension. The HCRF estimated optimum labels according to their spatial and hierarchical consistencies by using the layer-wise significant features determined over the trained HRF classifier. To handle variable sample numbers at different resolutions, resolution-specific hyperparameters were used. This method was trained and evaluated for segmenting 15 thoracic and lumbar VBs and their IVDs on fat-water MR images of a subset of a large cohort data set. It was further evaluated for segmenting seven IVDs of the lower spine on fat-water images of a public grand challenge. These evaluations revealed the comparable accuracy of this method with the state-of-the-art while requiring less computational burden due to a simultaneous localization and segmentation.