Abstract
We propose an automated method for supervised segmentation of vertebral bodies (VBs) from three-dimensional (3D) magnetic resonance (MR) spine images that is based on coupling deformable models with convolutional neural networks (CNNs). We designed a 3D CNN architecture that learns the appearance from a training set of VBs to generate 3D spatial VB probability maps, which guide deformable models towards VB boundaries. The proposed method was applied to segment 161 VBs from 3D MR spine images of 23 subjects, and the results were compared to reference segmentations. By yielding an overall Dice similarity coefficient of $$93.4\,{\pm }\,1.7\,\%$$ , mean symmetric surface distance of $$0.54\,{\pm }\,0.14\,\text {mm}$$ and Hausdorff distance of $$3.83\,{\pm }\,1.04\,\text {mm}$$ , the proposed method proved superior to existing VB segmentation methods.
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