Abstract #1485

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

Alexander J Daniel¹, Eleanor F Cox¹, Rachael A Evans², Louise V Wain², Christopher Brightling², Elizabeth Tunnicliffe^3,4, Stefan Neubauer^3,4, Betty Raman^3,4, and Susan T Francis¹

¹Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, ²Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, United Kingdom, ³Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford University, Oxford, United Kingdom, ⁴Oxford University Hospitals NHS Foundation Trust, Oxford University, Oxford, United Kingdom

Synopsis

Keywords: Kidney, Segmentation

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.

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