Automated Segmentation of Multi-Vendor Kidney Images using an Iteratively Trained Convolutional Neural Network

Measures of total kidney volume (TKV) help to evaluate disease progression, and masks to define the kidney are important for the automatic assessment of multiparametric images collected in the same data space. For accurate measures in multicentre studies an automated method which is vendor agnostic and robust against image artefacts is needed. Here a single-vendor convolutional neural network is retrained and shown to be accurate on two vendors of scanner and robust against image artefacts associated with wrapping in the phase-encode direction.