Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation

Owen Anderson, Andrew C. Kidd, Keith A. Goatman, Alexander J. Weir, Jeremy Voisey, Vismantas Dilys, Jan Paul Siebert, Kevin G. Blyth

Abstract: Malignant Pleural Mesothelioma (MPM) is a cancer associated with prior exposure to asbestos fibres. Unlike most tumours, which are roughly spherical, MPM grows like a rind surrounding the lung. This irregular shape poses significant clinical and technical challenges. Accurate tumour measurements are necessary to determine treatment efficacy, but manual segmentation is tedious, time-consuming and associated with high intra- and inter-observer variation. In addition, uncertainty is compounded by poor differentiation in the computed tomography (CT) image between MPM and other common features. We describe herein an internal validation of a fully automatic tool to generate volumetric segmentations of MPM tumours using a convolutional neural network (CNN). The system was trained using the first 123 CT volumetric datasets from a planned total of 403 scans. Each scan was manually segmented to provide the expert ground truth. Evaluation was by seven-fold cross validation on a subset