Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network

Nikolas Lessmann, Jelmer M. Wolterink, Majd Zreik, Max A. Viergever, Bram van Ginneken, Ivana Išgum

Keywords: thin-plate splines, shape analysis, vertebra partitioning, autoencoder

Abstract: Thin-plate splines can be used for interpolation of image values, but can also be used to represent a smooth surface, such as the boundary between two structures. We present a method for partitioning vertebra segmentation masks into two substructures, the vertebral body and the posterior elements, using a convolutional neural network that predicts the boundary between the two structures. This boundary is modeled as a thin-plate spline surface dened by a set of control points predicted by the network. The neural network is trained using the reconstruction error of a convolutional autoencoder to enable the use of unpaired data.

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