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.
Reflecting global interest in lung cancer screening, considerable attention has been paid to automatic segmentation and volumetric measurement of lung nodules on CT. Ground glass opacity (GGO) nodules deserve special consideration in this context, since it has been reported that they are more likely to be malignant than solid nodules. However, due to relatively low contrast and indistinct boundaries of GGO nodules, segmentation is more difficult for GGO nodules compared with solid nodules. To overcome this difficulty, we propose a method for accurately segmenting not only solid nodules but also GGO nodules without prior information about nodule types. First, the histogram of CT values in pre-extracted lung regions is modeled by a Gaussian mixture model and a threshold value for including high-attenuation regions is computed. Second, after setting up a region of interest around the nodule seed point, foreground regions are extracted by using the threshold and quick-shift-based mode seeking. Finally, for separating vessels from the nodule, a vessel-likelihood map derived from elongatedness of foreground regions is computed, and a region growing scheme starting from the seed point is applied to the map with the aid of fast marching method. Experimental results using an anthropomorphic chest phantom showed that our method yielded generally lower volumetric measurement errors for both solid and GGO nodules compared with other methods reported in preceding studies conducted using similar technical settings. Also, our method allowed reasonable segmentation of GGO nodules in low-dose images and could be applied to clinical CT images including part-solid nodules.
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