3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method

In the management of lung nodules, it is important to precisely assess nodule size on computed tomography (CT) images. Given that the malignancy of nodules varies according to their composition, component-wise assessment is useful for diagnosing lung cancer. To improve the accuracy of volumetric measurement of lung nodules, we propose a deep learning-based method for segmenting nodules into multiple components, namely, solid, ground glass opacity (GGO), and cavity. We train a 3D fully convolutional network (FCN) with component-wise dice loss and apply a conditional random field (CRF) to refine the segmentation boundaries. To further gain the accuracy, we artificially generate synthetic cavitary nodules based on clinical observations and then augment the dataset for training the network. In experiments using about 300 CT images of clinical nodules, we evaluated our method in terms of mean absolute percentage error of volumetric measurement. We confirmed that our method achieved 15.84% lower error (averaged over 2 components of solid and GGO) compared with a conventional method based on image processing, and the error for cavity was decreased by 2.87% with our data-synthesis method.