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The timely detection and segmentation of pulmonary nodules in lung computed tomography (CT) images can aid in the early diagnosis and treatment of lung cancer. However, manual segmentation of pulmonary nodules by doctors is highly demanding in terms of operational requirements and efficiency. To effectively improve the pulmonary nodule segmentation, this paper proposes a novel neural network, called ResDSda&#x005F;U-Net, based on the original U-Net network with the following improvements: (1) combining a Depthwise Over-parameterized Convolutional layer (DO-Conv) with a simple parameter-free attention module (SimAM), in the form of a newly designed ResDS block; (2) introducing a dense atrous spatial pyramid pooling (DASPP) module, between the encoder and decoder, using modified dilated rates to extract multi-scale information more effectively; and (3) introducing channel and spatial attention mechanisms in the decoder, in the form of newly designed Convolution and Channel Attention (CCA) and Convolution and Spatial Attention (CSA) blocks, to enhance global pixel attention, fully capture global contextual information, and enable the decoder to better eliminate differences between pixels. The conducted experiments demonstrate that the proposed ResDSda&#x005F;U-Net network outperforms all existing U-Net based networks (according to all evaluation metrics used) and all considered state-of-the-art networks (according to half of the evaluation metrics), by achieving corresponding values of 86.65% for the Dice Similarity Coefficient (DSC), 76.73% for Intersection over Union (IoU), 86.30% for sensitivity, and 87.22% for precision.

ResDSda_U-Net: A novel U-Net based residual network for segmentation of pulmonary nodules in lung CT images