The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6–72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients. Segmentation accuracy of GTV contours for 192 lung cancer patients [with the following tumor types: 118 solid, 53 part-solid types and 21 pure ground-glass opacity (pure GGO)], who underwent SBRT, were evaluated based on a 10-fold cross-validation test using Dice’s similarity coefficient (DSC) and Hausdorff distance (HD). For each case, 11 segmented GTVs consisting of three single outputs, four logical AND outputs, and four logical OR outputs from combinations of two or three outputs from DVNs were obtained by three runs with different initial weights. The AND output (combination of three outputs) achieved the highest values of average 3D-DSC (0.832 ± 0.074) and HD (4.57 ± 2.44 mm). The average 3D DSCs from the AND output for solid, part-solid and pure GGO types were 0.838 ± 0.074, 0.822 ± 0.078 and 0.819 ± 0.059, respectively. This study suggests that the proposed approach could be useful in segmenting GTVs for planning lung cancer SBRT.
Research on classification and segmentation of 3-D point clouds using deep learning methods has become a hot topic in emerging applications, such as autonomous driving, augmented reality, and indoor navigation. However, as the complexity of the network structures increases, the computational efficiency reduces, which affects the practical applications of these methods. In addition, prior researchers mostly seek to enhance the quality of spatial encodings, while the channel relationships are ignored. It makes the feature learning of point clouds insufficient, which will reduce the accuracy of classification and segmentation. In this article, a lightweight attention module (LAM) is proposed to improve the computational efficiency and accuracy at the same time by adopting a novel convolution mode and introducing a new attention mechanism based on channelwise statistical features. As the submodules of LAM, the lightweight module and the attention module can also be used independently to focus on improving the computational efficiency and accuracy, respectively, according to the actual applications. LAM and its submodules can be easily integrated into state-of-the-art deep learning methods on classification and segmentation of 3-D point clouds. The experimental results show that the proposed modules have a good performance on benchmark data sets.
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