Lung cancer has traditionally exhibited high incidence and mortality rates, making the early detection and treatment of the disease essential in reducing mortality rates. We present a new network design to enhance the separation of lung nodules in CT lung images. The design reduces the problem of missed or incorrect segmentation due to unclear nodule morphology, varying shapes, and attachment to the pleura. In this paper, the V-Net serves as the foundation network, paired with a multi-scale feature network to enhance the original's leap connections. Two distinct attention mechanism modules are integrated into the network encoding and decoding for increased feature extraction of lung nodules. The Log-Cosh Dice Loss replaces the original loss function to address the issue of non-convexity in the Dice loss function. Additionally, the lung 3D images are cropped to resolve the problem of imbalanced distribution of positive and negative samples within lung CT images. We evaluated the performance of the model on the LUNA16 dataset. The evaluation results demonstrate the superiority of the model. We observed objective improvements compared to the initial network; 6.9% improvement in DSC values, 11.5% improvement in MIoU values, 8.5% improvement in accuracy and 99.8% pixel accuracy. This method has been found to effectively prevent missed lung nodules and produce satisfactory segmentation results.

Xiaoru Xu, Lingyan Du, Dongsheng Yin. Segmentation of nodules in CT images of the lung using an improved V-Net network model. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 26-34. https://doi.org/10.25236/AJCIS.2023.061104.

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